Duplicate from ml6team/post-processing-summarization

Co-authored-by: Matthias Cami <[email protected]>

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.gitignore

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+*.idea
+__pycache__

README.md

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+---
+title: Hallucination detection in summaries
+emoji: 💡
+colorFrom: grey
+colorTo: red
+sdk: streamlit
+sdk_version: 1.2.0
+app_file: app.py
+pinned: false
+duplicated_from: ml6team/post-processing-summarization
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

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+from typing import AnyStr, Dict
+
+import itertools
+import streamlit as st
+import en_core_web_lg
+
+import torch.nn.parameter
+from bs4 import BeautifulSoup
+import numpy as np
+import base64
+
+from spacy_streamlit.util import get_svg
+from streamlit.proto.SessionState_pb2 import SessionState
+
+from custom_renderer import render_sentence_custom
+from sentence_transformers import SentenceTransformer
+
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+import os
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; 
+margin-bottom: 2.5rem">{}</div> """
+
+
+@st.experimental_singleton
+def get_sentence_embedding_model():
+    return SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+
+@st.experimental_singleton
+def get_spacy():
+    nlp = en_core_web_lg.load()
+    return nlp
+
+
+@st.experimental_singleton
+def get_transformer_pipeline():
+    tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
+    model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
+    return pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
+
+
+@st.experimental_singleton
+def get_summarizer_model():
+    model_name = 'google/pegasus-cnn_dailymail'
+    summarizer_model = pipeline("summarization", model=model_name, tokenizer=model_name,
+                                device=0 if torch.cuda.is_available() else -1)
+
+    return summarizer_model
+
+
+# Page setup
+st.set_page_config(
+    page_title="📜 Hallucination detection in summaries 📜",
+    page_icon="",
+    layout="centered",
+    initial_sidebar_state="auto",
+    menu_items={
+        'Get help': None,
+        'Report a bug': None,
+        'About': None,
+    }
+)
+
+
+def list_all_article_names() -> list:
+    filenames = []
+    for file in sorted(os.listdir('./sample-articles/')):
+        if file.endswith('.txt'):
+            filenames.append(file.replace('.txt', ''))
+    # Append free use possibility:
+    filenames.append("Provide your own input")
+    return filenames
+
+
+def fetch_article_contents(filename: str) -> AnyStr:
+    if filename == "Provide your own input":
+        return " "
+    with open(f'./sample-articles/{filename}.txt', 'r') as f:
+        data = f.read()
+    return data
+
+
+def fetch_summary_contents(filename: str) -> AnyStr:
+    with open(f'./sample-summaries/{filename}.txt', 'r') as f:
+        data = f.read()
+    return data
+
+
+def fetch_entity_specific_contents(filename: str) -> AnyStr:
+    with open(f'./entity-specific-text/{filename}.txt', 'r') as f:
+        data = f.read()
+    return data
+
+
+def fetch_dependency_specific_contents(filename: str) -> AnyStr:
+    with open(f'./dependency-specific-text/{filename}.txt', 'r') as f:
+        data = f.read()
+    return data
+
+
+def fetch_ranked_summaries(filename: str, ranknumber: int) -> AnyStr:
+    with open(f'./ranked-summaries/{filename}/Rank{ranknumber}.txt', 'r') as f:
+        data = f.read()
+    return data
+
+
+def fetch_dependency_svg(filename: str) -> AnyStr:
+    with open(f'./dependency-images/{filename}.txt', 'r') as f:
+        lines = [line.rstrip() for line in f]
+    return lines
+
+
+def display_summary(summary_content: str):
+    st.session_state.summary_output = summary_content
+    soup = BeautifulSoup(summary_content, features="html.parser")
+    return HTML_WRAPPER.format(soup)
+
+
+def get_all_entities_per_sentence(text):
+    doc = nlp(text)
+
+    sentences = list(doc.sents)
+
+    entities_all_sentences = []
+    for sentence in sentences:
+        entities_this_sentence = []
+
+        # SPACY ENTITIES
+        for entity in sentence.ents:
+            entities_this_sentence.append(str(entity))
+
+        # FLAIR ENTITIES (CURRENTLY NOT USED)
+        # sentence_entities = Sentence(str(sentence))
+        # tagger.predict(sentence_entities)
+        # for entity in sentence_entities.get_spans('ner'):
+        #     entities_this_sentence.append(entity.text)
+
+        # XLM ENTITIES
+        entities_xlm = [entity["word"] for entity in ner_model(str(sentence))]
+        for entity in entities_xlm:
+            entities_this_sentence.append(str(entity))
+
+        entities_all_sentences.append(entities_this_sentence)
+
+    return entities_all_sentences
+
+
+def get_all_entities(text):
+    all_entities_per_sentence = get_all_entities_per_sentence(text)
+    return list(itertools.chain.from_iterable(all_entities_per_sentence))
+
+
+def get_and_compare_entities(first_time: bool):
+    if first_time:
+        article_content = st.session_state.article_text
+        all_entities_per_sentence = get_all_entities_per_sentence(article_content)
+        entities_article = list(itertools.chain.from_iterable(all_entities_per_sentence))
+        st.session_state.entities_article = entities_article
+    else:
+        entities_article = st.session_state.entities_article
+
+    summary_content = st.session_state.summary_output
+    all_entities_per_sentence = get_all_entities_per_sentence(summary_content)
+    entities_summary = list(itertools.chain.from_iterable(all_entities_per_sentence))
+
+    matched_entities = []
+    unmatched_entities = []
+    for entity in entities_summary:
+        if any(entity.lower() in substring_entity.lower() for substring_entity in entities_article):
+            matched_entities.append(entity)
+        elif any(
+                np.inner(sentence_embedding_model.encode(entity, show_progress_bar=False),
+                         sentence_embedding_model.encode(art_entity, show_progress_bar=False)) > 0.9 for
+                art_entity in entities_article):
+            matched_entities.append(entity)
+        else:
+            unmatched_entities.append(entity)
+
+    matched_entities = list(dict.fromkeys(matched_entities))
+    unmatched_entities = list(dict.fromkeys(unmatched_entities))
+
+    matched_entities_to_remove = []
+    unmatched_entities_to_remove = []
+
+    for entity in matched_entities:
+        for substring_entity in matched_entities:
+            if entity != substring_entity and entity.lower() in substring_entity.lower():
+                matched_entities_to_remove.append(entity)
+
+    for entity in unmatched_entities:
+        for substring_entity in unmatched_entities:
+            if entity != substring_entity and entity.lower() in substring_entity.lower():
+                unmatched_entities_to_remove.append(entity)
+
+    matched_entities_to_remove = list(dict.fromkeys(matched_entities_to_remove))
+    unmatched_entities_to_remove = list(dict.fromkeys(unmatched_entities_to_remove))
+
+    for entity in matched_entities_to_remove:
+        matched_entities.remove(entity)
+    for entity in unmatched_entities_to_remove:
+        unmatched_entities.remove(entity)
+
+    return matched_entities, unmatched_entities
+
+
+def highlight_entities():
+    summary_content = st.session_state.summary_output
+    markdown_start_red = "<mark class=\"entity\" style=\"background: rgb(238, 135, 135);\">"
+    markdown_start_green = "<mark class=\"entity\" style=\"background: rgb(121, 236, 121);\">"
+    markdown_end = "</mark>"
+
+    matched_entities, unmatched_entities = get_and_compare_entities(True)
+
+    for entity in matched_entities:
+        summary_content = summary_content.replace(entity, markdown_start_green + entity + markdown_end)
+
+    for entity in unmatched_entities:
+        summary_content = summary_content.replace(entity, markdown_start_red + entity + markdown_end)
+    soup = BeautifulSoup(summary_content, features="html.parser")
+    return HTML_WRAPPER.format(soup)
+
+
+def highlight_entities_new(summary_str: str):
+    st.session_state.summary_output = summary_str
+    summary_content = st.session_state.summary_output
+    markdown_start_red = "<mark class=\"entity\" style=\"background: rgb(238, 135, 135);\">"
+    markdown_start_green = "<mark class=\"entity\" style=\"background: rgb(121, 236, 121);\">"
+    markdown_end = "</mark>"
+
+    matched_entities, unmatched_entities = get_and_compare_entities(False)
+
+    for entity in matched_entities:
+        summary_content = summary_content.replace(entity, markdown_start_green + entity + markdown_end)
+
+    for entity in unmatched_entities:
+        summary_content = summary_content.replace(entity, markdown_start_red + entity + markdown_end)
+    soup = BeautifulSoup(summary_content, features="html.parser")
+    return HTML_WRAPPER.format(soup)
+
+
+def render_dependency_parsing(text: Dict):
+    html = render_sentence_custom(text, nlp)
+    html = html.replace("\n\n", "\n")
+    st.write(get_svg(html), unsafe_allow_html=True)
+
+
+def check_dependency(article: bool):
+    if article:
+        text = st.session_state.article_text
+        all_entities = get_all_entities_per_sentence(text)
+    else:
+        text = st.session_state.summary_output
+        all_entities = get_all_entities_per_sentence(text)
+    doc = nlp(text)
+    tok_l = doc.to_json()['tokens']
+    test_list_dict_output = []
+
+    sentences = list(doc.sents)
+    for i, sentence in enumerate(sentences):
+        start_id = sentence.start
+        end_id = sentence.end
+        for t in tok_l:
+            if t["id"] < start_id or t["id"] > end_id:
+                continue
+            head = tok_l[t['head']]
+            if t['dep'] == 'amod' or t['dep'] == "pobj":
+                object_here = text[t['start']:t['end']]
+                object_target = text[head['start']:head['end']]
+                if t['dep'] == "pobj" and str.lower(object_target) != "in":
+                    continue
+                # ONE NEEDS TO BE ENTITY
+                if object_here in all_entities[i]:
+                    identifier = object_here + t['dep'] + object_target
+                    test_list_dict_output.append({"dep": t['dep'], "cur_word_index": (t['id'] - sentence.start),
+                                                  "target_word_index": (t['head'] - sentence.start),
+                                                  "identifier": identifier, "sentence": str(sentence)})
+                elif object_target in all_entities[i]:
+                    identifier = object_here + t['dep'] + object_target
+                    test_list_dict_output.append({"dep": t['dep'], "cur_word_index": (t['id'] - sentence.start),
+                                                  "target_word_index": (t['head'] - sentence.start),
+                                                  "identifier": identifier, "sentence": str(sentence)})
+                else:
+                    continue
+    return test_list_dict_output
+
+
+def render_svg(svg_file):
+    with open(svg_file, "r") as f:
+        lines = f.readlines()
+        svg = "".join(lines)
+
+        # """Renders the given svg string."""
+        b64 = base64.b64encode(svg.encode("utf-8")).decode("utf-8")
+        html = r'<img src="data:image/svg+xml;base64,%s"/>' % b64
+        return html
+
+
+def generate_abstractive_summary(text, type, min_len=120, max_len=512, **kwargs):
+    text = text.strip().replace("\n", " ")
+    if type == "top_p":
+        text = summarization_model(text, min_length=min_len,
+                                   max_length=max_len,
+                                   top_k=50, top_p=0.95, clean_up_tokenization_spaces=True, truncation=True, **kwargs)
+    elif type == "greedy":
+        text = summarization_model(text, min_length=min_len,
+                                   max_length=max_len, clean_up_tokenization_spaces=True, truncation=True, **kwargs)
+    elif type == "top_k":
+        text = summarization_model(text, min_length=min_len, max_length=max_len, top_k=50,
+                                   clean_up_tokenization_spaces=True, truncation=True, **kwargs)
+    elif type == "beam":
+        text = summarization_model(text, min_length=min_len,
+                                   max_length=max_len,
+                                   clean_up_tokenization_spaces=True, truncation=True, **kwargs)
+    summary = text[0]['summary_text'].replace("<n>", " ")
+    return summary
+
+
+# Load all different models (cached) at start time of the hugginface space
+sentence_embedding_model = get_sentence_embedding_model()
+ner_model = get_transformer_pipeline()
+nlp = get_spacy()
+summarization_model = get_summarizer_model()
+
+# Page
+st.title('📜 Hallucination detection 📜')
+st.subheader("🔎 Detecting errors in generated abstractive summaries")
+#st.title('📜 Error detection in summaries 📜')
+
+# INTRODUCTION
+st.header("🧑‍🏫 Introduction")
+
+#introduction_checkbox = st.checkbox("Show introduction text", value=True)
+#if introduction_checkbox:
+st.markdown("""
+Recent work using 🤖 **transformers** 🤖 on large text corpora has shown great success when fine-tuned on 
+several different downstream NLP tasks. One such task is that of text summarization. The goal of text summarization 
+is to generate concise and accurate summaries from input document(s). There are 2 types of summarization:
+
+ - **Extractive summarization** merely copies informative fragments from the input. 
+ - **Abstractive summarization** 
+ may generate novel words. A good abstractive summary should cover principal information in the input and has to be 
+ linguistically fluent. This interactive blogpost will focus on this more difficult task of abstractive summary 
+ generation. Furthermore we will focus mainly on hallucination errors, and less on sentence fluency.""")
+
+st.markdown("###")
+st.markdown("🤔 **Why is this important?** 🤔 Let's say we want to summarize news articles for a popular "
+            "newspaper. If an article tells the story of Elon Musk buying **Twitter**, we don't want our summarization "
+            "model to say that he bought **Facebook** instead. Summarization could also be done for financial reports "
+            "for example. In such environments, these errors can be very critical, so we want to find a way to "
+            "detect them.")
+st.markdown("###")
+st.markdown("""To generate summaries we will use the 🐎 [PEGASUS](https://huggingface.co/google/pegasus-cnn_dailymail) 🐎
+model, producing abstractive summaries from large articles. These summaries often contain sentences with different 
+kinds of errors. Rather than improving the core model, we will look into possible post-processing steps to detect errors 
+from the generated summaries. Throughout this blog, we will also explain the results for some methods on specific 
+examples. These text blocks will be indicated and they change according to the currently selected article.""")
+
+# GENERATING SUMMARIES PART
+st.header("🪶 Generating summaries")
+st.markdown("Let’s start by selecting an article text for which we want to generate a summary, or you can provide "
+            "text yourself. Note that it’s suggested to provide a sufficiently large article, as otherwise the "
+            "summary generated from it might not be optimal, leading to suboptimal performance of the post-processing "
+            "steps. However, too long articles will be truncated and might miss information in the summary.")
+
+st.markdown("####")
+selected_article = st.selectbox('Select an article or provide your own:',
+                                list_all_article_names(), index=2)
+st.session_state.article_text = fetch_article_contents(selected_article)
+article_text = st.text_area(
+    label='Full article text',
+    value=st.session_state.article_text,
+    height=250
+)
+
+summarize_button = st.button(label='🤯 Process article content',
+                             help="Start interactive blogpost")
+
+if summarize_button:
+    st.session_state.article_text = article_text
+    st.markdown("####")
+    st.markdown(
+        "*Below you can find the generated summary for the article. We will discuss two approaches that we found are "
+        "able to detect some common errors. Based on these errors, one could then score different summaries, indicating how "
+        "factual a summary is for a given article. The idea is that in production, you could generate a set of "
+        "summaries for the same article, with different parameters (or even different models). By using "
+        "post-processing error detection, we can then select the best possible summary.*")
+    st.markdown("####")
+    if st.session_state.article_text:
+        with st.spinner('Generating summary, this might take a while...'):
+            if selected_article != "Provide your own input" and article_text == fetch_article_contents(
+                    selected_article):
+                st.session_state.unchanged_text = True
+                summary_content = fetch_summary_contents(selected_article)
+            else:
+                summary_content = generate_abstractive_summary(article_text, type="beam", do_sample=True, num_beams=15,
+                                                               no_repeat_ngram_size=4)
+                st.session_state.unchanged_text = False
+            summary_displayed = display_summary(summary_content)
+            st.write("✍ **Generated summary:** ✍", summary_displayed, unsafe_allow_html=True)
+    else:
+        st.error('**Error**: No comment to classify. Please provide a comment.')
+
+    # ENTITY MATCHING PART
+    st.header("1️⃣ Entity matching")
+    st.markdown("The first method we will discuss is called **Named Entity Recognition** (NER). NER is the task of "
+                "identifying and categorising key information (entities) in text. An entity can be a singular word or a "
+                "series of words that consistently refers to the same thing. Common entity classes are person names, "
+                "organisations, locations and so on. By applying NER to both the article and its summary, we can spot "
+                "possible **hallucinations**. ")
+
+    st.markdown("Hallucinations are words generated by the model that are not supported by "
+                "the source input. Deep learning based generation is [prone to hallucinate]("
+                "https://arxiv.org/pdf/2202.03629.pdf) unintended text. These hallucinations degrade "
+                "system performance and fail to meet user expectations in many real-world scenarios. By applying entity matching, we can improve this problem"
+                " for the downstream task of summary generation.")
+
+    st.markdown(" In theory all entities in the summary (such as dates, locations and so on), "
+                "should also be present in the article. Thus we can extract all entities from the summary and compare "
+                "them to the entities of the original article, spotting potential hallucinations. The more unmatched "
+                "entities we find, the lower the factualness score of the summary. ")
+    with st.spinner("Calculating and matching entities, this takes about 10-20 seconds..."):
+        entity_match_html = highlight_entities()
+        st.markdown("####")
+        st.write(entity_match_html, unsafe_allow_html=True)
+        red_text = """<font color="black"><span style="background-color: rgb(238, 135, 135); opacity: 
+        1;">red</span></font> """
+        green_text = """<font color="black">
+            <span style="background-color: rgb(121, 236, 121); opacity: 1;">green</span>
+        </font>"""
+
+        markdown_start_red = "<mark class=\"entity\" style=\"background: rgb(238, 135, 135);\">"
+        markdown_start_green = "<mark class=\"entity\" style=\"background: rgb(121, 236, 121);\">"
+        st.markdown(
+            "We call this technique **entity matching** and here you can see what this looks like when we apply this "
+            "method on the summary. Entities in the summary are marked  " + green_text + " when the entity also "
+                                                                                         "exists in the article, "
+                                                                                         "while unmatched entities "
+                                                                                         "are marked " + red_text +
+            ". Several of the example articles and their summaries indicate different errors we find by using this "
+            "technique. Based on the current article, we provide a short explanation of the results below **(only for "
+            "example articles)**. ", unsafe_allow_html=True)
+        if st.session_state.unchanged_text:
+            entity_specific_text = fetch_entity_specific_contents(selected_article)
+            soup = BeautifulSoup(entity_specific_text, features="html.parser")
+            st.markdown("####")
+            st.write("💡👇 **Specific example explanation** 👇💡", HTML_WRAPPER.format(soup), unsafe_allow_html=True)
+
+    # DEPENDENCY PARSING PART
+    st.header("2️⃣ Dependency comparison")
+    st.markdown(
+        "The second method we use for post-processing is called **Dependency Parsing**: the process in which the "
+        "grammatical structure in a sentence is analysed, to find out related words as well as the type of the "
+        "relationship between them. For the sentence “Jan’s wife is called Sarah” you would get the following "
+        "dependency graph:")
+
+    # TODO: I wonder why the first doesn't work but the second does (it doesn't show deps otherwise)
+    # st.image("ExampleParsing.svg")
+    st.write(render_svg('ExampleParsing.svg'), unsafe_allow_html=True)
+    st.markdown(
+        "Here, *“Jan”* is the *“poss”* (possession modifier) of *“wife”*. If suddenly the summary would read *“Jan’s"
+        " husband…”*, there would be a dependency in the summary that is non-existent in the article itself (namely "
+        "*“Jan”* is the “poss” of *“husband”*)."
+        "However, often new dependencies are introduced in the summary that "
+        "are still correct, as can be seen in the example below. ")
+    st.write(render_svg('SecondExampleParsing.svg'), unsafe_allow_html=True)
+
+    st.markdown("*“The borders of Ukraine”* have a different dependency between *“borders”* and "
+                "*“Ukraine”* "
+                "than *“Ukraine’s borders”*, while both descriptions have the same meaning. So just matching all "
+                "dependencies between article and summary (as we did with entity matching) would not be a robust method."
+                " More on the different sorts of dependencies and their description can be found [here](https://universaldependencies.org/docs/en/dep/).")
+    st.markdown("However, we have found that **there are specific dependencies that are often an "
+                "indication of a wrongly constructed sentence** when there is no article match. We (currently) use 2 "
+                "common dependencies which - when present in the summary but not in the article - are highly "
+                "indicative of factualness errors. "
+                "Furthermore, we only check dependencies between an existing **entity** and its direct connections. "
+                "Below we highlight all unmatched dependencies that satisfy the discussed constraints. We also "
+                "discuss the specific results for the currently selected example article.")
+    with st.spinner("Doing dependency parsing..."):
+        if st.session_state.unchanged_text:
+            for cur_svg_image in fetch_dependency_svg(selected_article):
+                st.write(cur_svg_image, unsafe_allow_html=True)
+            dep_specific_text = fetch_dependency_specific_contents(selected_article)
+            soup = BeautifulSoup(dep_specific_text, features="html.parser")
+            st.write("💡👇 **Specific example explanation** 👇💡", HTML_WRAPPER.format(soup), unsafe_allow_html=True)
+        else:
+            summary_deps = check_dependency(False)
+            article_deps = check_dependency(True)
+            total_unmatched_deps = []
+            for summ_dep in summary_deps:
+                if not any(summ_dep['identifier'] in art_dep['identifier'] for art_dep in article_deps):
+                    total_unmatched_deps.append(summ_dep)
+            if total_unmatched_deps:
+                for current_drawing_list in total_unmatched_deps:
+                    render_dependency_parsing(current_drawing_list)
+
+    # CURRENTLY DISABLED
+    # OUTRO/CONCLUSION
+    st.header("🤝 Bringing it together")
+    st.markdown("We have presented 2 methods that try to detect errors in summaries via post-processing steps. Entity "
+                "matching can be used to solve hallucinations, while dependency comparison can be used to filter out "
+                "some bad sentences (and thus worse summaries). These methods highlight the possibilities of "
+                "post-processing AI-made summaries, but are only a first introduction. As the methods were "
+                "empirically tested they are definitely not sufficiently robust for general use-cases.")
+    st.markdown("####")
+    st.markdown(
+        "*Below we generate 3 different kind of summaries, and based on the two discussed methods, their errors are "
+        "detected to estimate a summary score. Based on this basic approach, "
+        "the best summary (read: the one that a human would prefer or indicate as the best one) "
+        "will hopefully be at the top. We currently "
+        "only do this for the example articles (for which the different summmaries are already generated). The reason "
+        "for this is that HuggingFace spaces are limited in their CPU memory. We also highlight the entities as done "
+        "before, but note that the rankings are done on a combination of unmatched entities and "
+        "dependencies (with the latter not shown here).*")
+    st.markdown("####")
+
+    if selected_article != "Provide your own input" and article_text == fetch_article_contents(selected_article):
+        with st.spinner("Fetching summaries, ranking them and highlighting entities, this might take a minute or two..."):
+            summaries_list = []
+            deduction_points = []
+
+            # FOR NEW GENERATED SUMMARY
+            for i in range(1 , 4):
+                st.session_state.summary_output = fetch_ranked_summaries(selected_article, i)
+                _, amount_unmatched = get_and_compare_entities(False)
+
+                summary_deps = check_dependency(False)
+                article_deps = check_dependency(True)
+                total_unmatched_deps = []
+                for summ_dep in summary_deps:
+                    if not any(summ_dep['identifier'] in art_dep['identifier'] for art_dep in article_deps):
+                        total_unmatched_deps.append(summ_dep)
+
+                summaries_list.append(st.session_state.summary_output)
+                deduction_points.append(len(amount_unmatched) + len(total_unmatched_deps))
+
+
+            # RANKING AND SHOWING THE SUMMARIES
+            deduction_points, summaries_list = (list(t) for t in zip(*sorted(zip(deduction_points, summaries_list))))
+
+            cur_rank = 1
+            rank_downgrade = 0
+            for i in range(len(deduction_points)):
+                #st.write(f'🏆 Rank {cur_rank} summary: 🏆', display_summary(summaries_list[i]), unsafe_allow_html=True)
+                st.write(f'🏆 Rank {cur_rank} summary: 🏆', highlight_entities_new(summaries_list[i]), unsafe_allow_html=True)
+                if i < len(deduction_points) - 1:
+                    rank_downgrade += 1
+                    if not deduction_points[i + 1] == deduction_points[i]:
+                        cur_rank += rank_downgrade
+                        rank_downgrade = 0

custom_renderer.py

@@ -0,0 +1,133 @@
+from typing import Dict
+from PIL import ImageFont
+
+TPL_DEP_WORDS = """
+<text class="displacy-token" fill="currentColor" text-anchor="start" y="{y}">
+    <tspan class="displacy-word" fill="currentColor" x="{x}">{text}</tspan>
+    <tspan class="displacy-tag" dy="2em" fill="currentColor" x="{x}">{tag}</tspan>
+</text>
+"""
+
+TPL_DEP_SVG = """
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:lang="{lang}" id="{id}" class="displacy" width="{width}" height="{height}" direction="{dir}" style="max-width: none; height: {height}px; color: {color}; background: {bg}; font-family: {font}; direction: {dir}">{content}</svg>
+"""
+
+TPL_DEP_ARCS = """
+<g class="displacy-arrow">
+    <path class="displacy-arc" id="arrow-{id}-{i}" stroke-width="{stroke}px" d="{arc}" fill="none" stroke="red"/>
+    <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+        <textPath xlink:href="#arrow-{id}-{i}" class="displacy-label" startOffset="50%" side="{label_side}" fill="red" text-anchor="middle">{label}</textPath>
+    </text>
+    <path class="displacy-arrowhead" d="{head}" fill="red"/>
+</g>
+"""
+
+
+def get_pil_text_size(text, font_size, font_name):
+    font = ImageFont.truetype(font_name, font_size)
+    size = font.getsize(text)
+    return size
+
+
+def render_arrow(
+        label: str, start: int, end: int, direction: str, i: int
+) -> str:
+    """Render individual arrow.
+
+    label (str): Dependency label.
+    start (int): Index of start word.
+    end (int): Index of end word.
+    direction (str): Arrow direction, 'left' or 'right'.
+    i (int): Unique ID, typically arrow index.
+    RETURNS (str): Rendered SVG markup.
+    """
+
+    arc = get_arc(start + 10, 50, 5, end + 10)
+    arrowhead = get_arrowhead(direction, start + 10, 50, end + 10)
+    label_side = "right" if direction == "rtl" else "left"
+    return TPL_DEP_ARCS.format(
+        id=0,
+        i=0,
+        stroke=2,
+        head=arrowhead,
+        label=label,
+        label_side=label_side,
+        arc=arc,
+    )
+
+
+def get_arc(x_start: int, y: int, y_curve: int, x_end: int) -> str:
+    """Render individual arc.
+
+    x_start (int): X-coordinate of arrow start point.
+    y (int): Y-coordinate of arrow start and end point.
+    y_curve (int): Y-corrdinate of Cubic Bézier y_curve point.
+    x_end (int): X-coordinate of arrow end point.
+    RETURNS (str): Definition of the arc path ('d' attribute).
+    """
+    template = "M{x},{y} C{x},{c} {e},{c} {e},{y}"
+    return template.format(x=x_start, y=y, c=y_curve, e=x_end)
+
+
+def get_arrowhead(direction: str, x: int, y: int, end: int) -> str:
+    """Render individual arrow head.
+
+    direction (str): Arrow direction, 'left' or 'right'.
+    x (int): X-coordinate of arrow start point.
+    y (int): Y-coordinate of arrow start and end point.
+    end (int): X-coordinate of arrow end point.
+    RETURNS (str): Definition of the arrow head path ('d' attribute).
+    """
+    arrow_width = 6
+    if direction == "left":
+        p1, p2, p3 = (x, x - arrow_width + 2, x + arrow_width - 2)
+    else:
+        p1, p2, p3 = (end, end + arrow_width - 2, end - arrow_width + 2)
+    return f"M{p1},{y + 2} L{p2},{y - arrow_width} {p3},{y - arrow_width}"
+
+
+def render_sentence_custom(unmatched_list: Dict, nlp):
+    arcs_svg = []
+    doc = nlp(unmatched_list["sentence"])
+
+    x_value_counter = 10
+    index_counter = 0
+    svg_words = []
+    words_under_arc = []
+    direction_current = "rtl"
+
+    if unmatched_list["cur_word_index"] < unmatched_list["target_word_index"]:
+        min_index = unmatched_list["cur_word_index"]
+        max_index = unmatched_list["target_word_index"]
+        direction_current = "left"
+    else:
+        max_index = unmatched_list["cur_word_index"]
+        min_index = unmatched_list["target_word_index"]
+    for i, token in enumerate(doc):
+        word = str(token)
+        word = word + " "
+        pixel_x_length = get_pil_text_size(word, 16, 'arial.ttf')[0]
+        svg_words.append(TPL_DEP_WORDS.format(text=word, tag="", x=x_value_counter, y=70))
+        if min_index <= index_counter <= max_index:
+            words_under_arc.append(x_value_counter)
+            if index_counter < max_index - 1:
+                x_value_counter += 50
+        index_counter += 1
+        x_value_counter += pixel_x_length + 4
+
+    arcs_svg.append(render_arrow(unmatched_list['dep'], words_under_arc[0], words_under_arc[-1], direction_current, i))
+
+    content = "".join(svg_words) + "".join(arcs_svg)
+
+    full_svg = TPL_DEP_SVG.format(
+        id=0,
+        width=1200,  # 600
+        height=75,  # 125
+        color="#00000",
+        bg="#ffffff",
+        font="Arial",
+        content=content,
+        dir="ltr",
+        lang="en",
+    )
+    return full_svg

dependency-images/Mark Levinson Airpods.txt

@@ -0,0 +1 @@
+<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem"><img src="data:image/svg+xml;base64,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" style=""/></div>

dependency-images/Novak Djokovic.txt

@@ -0,0 +1 @@
+<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem"><img src="data:image/svg+xml;base64,
  <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:lang="en" id="0" class="displacy" width="1200" height="75" direction="ltr" style="max-width: none; height: 75px; color: #00000; background: #ffffff; font-family: Arial; direction: ltr">
  <text class="displacy-token" fill="currentColor" text-anchor="start" y="70">
      <tspan class="displacy-word" fill="currentColor" x="10">in </tspan>
      <tspan class="displacy-tag" dy="2em" fill="currentColor" x="10"></tspan>
  </text>
  
  <text class="displacy-token" fill="currentColor" text-anchor="start" y="70">
      <tspan class="displacy-word" fill="currentColor" x="31">Australia </tspan>
      <tspan class="displacy-tag" dy="2em" fill="currentColor" x="31"></tspan>
  </text>
  
  <text class="displacy-token" fill="currentColor" text-anchor="start" y="70">
      <tspan class="displacy-word" fill="currentColor" x="103">is </tspan>
      <tspan class="displacy-tag" dy="2em" fill="currentColor" x="103"></tspan>
  </text>
  
  <text class="displacy-token" fill="currentColor" text-anchor="start" y="70">
      <tspan class="displacy-word" fill="currentColor" x="123">seeing </tspan>
      <tspan class="displacy-tag" dy="2em" fill="currentColor" x="123"></tspan>
  </text>
  
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dependency-images/OnePlus 10 Pro.txt

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dependency-images/White House.txt

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dependency-specific-text/Mark Levinson Airpods.txt

@@ -0,0 +1,4 @@
+One of the dependencies that, when found in the summary but not in the article, indicates a possible error is the *"pobj"*(object of preposition) dependency.
+Furthermore, we only match *pobj* dependencies when the target word is *"in"*, as in this example.
+In this case it's obvious that *"in U.S."* is not found in the article, as *"U.S."* is a hallucinated entity itself as discussed in the entity matching paragraph.
+So technically we don't need dependency comparison to spot the error from this summary.

dependency-specific-text/Novak Djokovic.txt

@@ -0,0 +1,4 @@
+One of the dependencies that, when found in the summary but not in the article, indicates a possible error is the *"pobj"* (object of preposition) dependency.
+Furthermore, we only match *pobj* dependencies when the target word is *"in"*, as in this example.
+The sentence here is not a factual error per se, but rather a readability issue. The *"in"* should be dropped to make the sentence correct.
+For better examples with this specific dependency, try choosing another article.

dependency-specific-text/OnePlus 10 Pro.txt

@@ -0,0 +1,9 @@
+One of the dependencies that, when found in the summary but not in the article, indicates a possible error is the *"amod"* (adjectival modifier) dependency.
+Applied to this summary, we have *"First"* as the entity, and it is the adjectival modifier of the word *"phone"*.
+And indeed, this unmatched dependency indicates an actual error here. The sentence is not factual, since the article talks about a **new** type of flagship phone,
+and not the **first** flagship phone. This error was found by filtering on this specific kind of dependency. Empirical results showed that unmatched *amod* dependencies often suggest
+that the summary sentence contains an error.  <br> <br>
+Another dependency that we use is the *"pobj"* (object of preposition) dependency.
+Furthermore, we only match *pobj* dependencies when the target word is *"in"*, as in this example.
+In this case the sentence itself contains a factual error (because the article states *"there's no word on a US release date yet"*).
+However, this could have already been found with entity matching (as *January 18* is unmatched), and the unmatched dependency can not be completely blamed for this error here.

dependency-specific-text/Tencent Holdings.txt

@@ -0,0 +1 @@
+Nothing suspicious about this summary

dependency-specific-text/White House.txt

@@ -0,0 +1,7 @@
+One of the dependencies that, when found in the summary but not in the article, indicates a possible error is the *"amod"* (adjectival modifier) dependency.
+Applied to this summary, we have *"Democratic"* as the entity, and it is the adjectival modifier of the word *"member"*.
+And indeed, this unmatched dependency indicates an actual error here. The sentence is not factual for two reasons. <br> <br>
+First, the article talks about *"democrats"* and *"members of the committee"*, which are two separate things. The summary combines those two in a way
+that can be seen as not completely factual. Second, the statement itself was not made by a democrat (nor a member of the committee), and even though the dependency can't be
+directly linked to this error, empirical results showed that unmatched *amod* dependencies often suggest
+that the summary sentence is incorrect.

dependency-specific-text/example.txt

@@ -0,0 +1 @@
+This is example explanation.

entity-specific-text/Mark Levinson Airpods.txt

@@ -0,0 +1,2 @@
+As you can see we have 1 unmatched entity: *"U.S."* is a hallucinated entity in the summary, that does not exist in the article.
+This error can be found/solved by applying entity matching.

entity-specific-text/Novak Djokovic.txt

@@ -0,0 +1 @@
+Nothing suspicious about this summary

entity-specific-text/OnePlus 10 Pro.txt

@@ -0,0 +1,3 @@
+As you can see we have 2 unmatched entities: *"January 18"* and *"U.S"*. The first one is a hallucinated entity in the summary, that does not exist in the article.
+*U.S*. **does** occur in the article, but as *"US"* instead of *"U.S."*. This could be solved
+by comparing to a list of abbreviations or with a specific embedder for abbreviations but is currently not implemented.

entity-specific-text/Tencent Holdings.txt

@@ -0,0 +1,2 @@
+As you can see we have 1 unmatched entity: *"Six9"* is a hallucinated entity in the summary, that does not exist in the article.
+This error can be found/solved by applying entity matching.

entity-specific-text/White House.txt

@@ -0,0 +1 @@
+Nothing suspicious about this summary

ranked-summaries/Mark Levinson Airpods/Rank1.txt

@@ -0,0 +1 @@
+The Mark Levinson No. 5909 is the company's first wireless headphones. It packs 40mm Beryllium drivers "expertly tuned to the Harman curve" The headphones will be available in black, pewter and red color options starting today for $999. A quick-charge feature will give you up to six hours of play time in 15 minutes; an app for Android and iOS will give you some control over the headphones, but the company didn't go into specifics there. Harman-owned brand Mark Levinson also owns AKG, JBL and Harman Kardon.

ranked-summaries/Mark Levinson Airpods/Rank2.txt

@@ -0,0 +1 @@
+The Mark Levinson No. 5909 is the company's first wireless headphones. It packs 40mm Beryllium drivers and adaptive active noise cancellation. The headphones start at $999 and come in black, pewter and red color options. They'll be available starting today for the same price through Mark Levinson's website and Apple's site for pre-order. Harman-owned brand Mark Levinson also owns AKG, JBL and Harman Kardon, and is a Samsung subsidiary. The No. 5909 will be available in black, Pewter and red color options starting today for $999.

ranked-summaries/Mark Levinson Airpods/Rank3.txt

@@ -0,0 +1 @@
+The Mark Levinson No. 5909 is the company's first wireless headphones. It's "reference class" and comes in black, pewter and red color options.. The headphones start at $999 and will be available starting today in the U.S. A quick-charge feature will give you up to six hours of play time in 15 minutes, the company says, via an app for Android and iOS.The company also packed in four microphones for calls that are equipped with a so-called Smart Wind Adaption feature., via Bluetooth 5.1.

ranked-summaries/Novak Djokovic/Rank1.txt

@@ -0,0 +1 @@
+Novak Djokovic has been granted permission to play in the Australian Open. All players and staff at the tournament must be vaccinated or have an exemption. Djokovic has not spoken about his vaccination status, but last year said he was "opposed to vaccination" Australian Prime Minister Scott Morrison said there should be no special rules. He said Djokovic would be required to present evidence upon arrival that he has a genuine medical exemption from vaccination, or he would be "on the next plane home" The controversy comes as the country is seeing tens of thousands of Covid-19 cases for the first time.

ranked-summaries/Novak Djokovic/Rank2.txt

@@ -0,0 +1 @@
+Novak Djokovic has been granted permission to play in the Australian Open. All players and staff at the tournament must be vaccinated or have an exemption. Djokovic has not spoken about his vaccination status, but last year said he was "opposed to vaccination" Australian Prime Minister Scott Morrison said there should be no special rules. The controversy comes as the country is seeing tens of thousands of Covid-19 cases. Over 90% of Australia's over-16 population is fully vaccinated, but some Australians still cannot travel interstate or globally because of restrictions. A&E doctor Stephen Parnis tweeted: "I don't care how good a tennis player he is. If he's refusing to get vaccinated, he shouldn't be allowed in"

ranked-summaries/Novak Djokovic/Rank3.txt

@@ -0,0 +1 @@
+Novak Djokovic has been granted permission to play in the Australian Open. All players and staff at the tournament must be vaccinated or have an exemption.. Djokovic has not spoken about his vaccination status, but last year said he was "opposed to vaccination" Australian Prime Minister Scott Morrison says there should be no special rules for Djokovic, but adds he would be "on the next plane home" if he did not have the right evidence. in Australia is seeing tens of thousands of Covid-19 cases for the first time after enduring some of the world's strictest restrictions.

ranked-summaries/OnePlus 10 Pro/Rank1.txt

@@ -0,0 +1 @@
+The OnePlus 10 Pro is the company's first flagship phone. It will have the brand-new Qualcomm Snapdragon 8 Gen 1 processor. The phone is launching in China first on January 11. We don't have a price yet, but OnePlus' flagship prices have gone up every year so far. There's also no word on a US release date yet. This is not an official picture, but OnLeaks' clearly accurate render from November is still our only look at the front of the phone. We don't actually have a picture of the front yet, so above is OnLeaks' unofficial render from a few months ago. This has the camera hole on the left side instead of the middle. Other than that, it looks like every other Android phone on the market. It might be because of Oppo's influence, but OnePlus' launch is all sorts of weird this year.

ranked-summaries/OnePlus 10 Pro/Rank2.txt

@@ -0,0 +1 @@
+The OnePlus 10 Pro is the company's first flagship phone. It will have the brand-new Qualcomm Snapdragon 8 Gen 1 processor. The phone is launching in China first on January 11. There's no price yet, but the 9 Pro was $969. There's also no word on a US release date yet. The phone will go on sale in China on January 11 and then the U.S. later this year. We're already seeing the effects of the Oppo merger on the OnePlus 10 Pro. We got a glimpse of this design direction via the OnePlus 9's Android 12 update, and the reviews were not kind. But what really matters is the software, which will see OnePlus adopt Oppo's Color OS Android skin with a few custom tweaks rather than the separates the two companies were running.

ranked-summaries/OnePlus 10 Pro/Rank3.txt

@@ -0,0 +1 @@
+The OnePlus 10 Pro is the company's first flagship phone. It's the result of a merger between OnePlus and Oppo, which will be called "SuperVOOC" The phone is launching in China first on January 11. There's also no word on a US release date yet. The 10 Pro will have a 6.7-inch display and three cameras on the back. We don't have a price yet, but OnePlus' flagship prices have gone up every year so far, and the 9 Pro was $969. The phone will go on sale January 11 in China and January 18 in the U.S.

ranked-summaries/Tencent Holdings/Rank1.txt

@@ -0,0 +1 @@
+Tencent said late on Tuesday it had entered into a deal to reduce its stake in the Singapore-based gaming and e-commerce group to 18.7% from 21.3%. The company plans to retain the substantial majority of its stake in Sea for the long term. The sale comes after Tencent said last month it would divest $16.4 billion of its stake in JD.com. Sea's shares fell 11.4% on Tuesday in New York to $197.8 following the divestment news. Tencent will be subject to a lockup period that restricts further sale of Sea shares by Tencent during the next six months.

ranked-summaries/Tencent Holdings/Rank2.txt

@@ -0,0 +1 @@
+Tencent said late on Tuesday it had entered into a deal to reduce its stake in the Singapore-based gaming and e-commerce group to 18.7% from 21.3%. The company plans to retain the substantial majority of its stake in Sea for the long term. The sale comes after Tencent said last month it would divest $16.4 billion of its stake in JD.com. Sea's shares fell 11.4% on Tuesday in New York to $197.8 following the divestment news. Sea is expanding its e-commerce operations globally, benefiting from roaring demand for its services from consumers.

ranked-summaries/Tencent Holdings/Rank3.txt

@@ -0,0 +1 @@
+Tencent Holdings Ltd has raised $3 billion by selling 14.5 million shares in Sea. Sea owns e-commerce firm Shopee, according to a term sheet seen by Reuters on Wednesday. Tencent said late on Tuesday it had entered into a deal to reduce its stake in the Singapore-based group to 18.7% from 21.3%. The sale comes after Tencent said last month it would divest $16.4 billion of its stakes in JD.com and Six9, weakening its ties to China's second-biggest e- commerce firm. SEA's shares fell 11.4% on Tuesday in New York to $197.8 following the divestment news.

ranked-summaries/White House/Rank1.txt

@@ -0,0 +1 @@
+Mark Meadows will no longer cooperate with the House select committee, according to a letter from his attorney. The committee says it will move forward with a scheduled deposition with Meadows on Wednesday. By proceeding with the scheduled deposition, the committee is setting up a path to hold Meadows in criminal contempt. The panel is weighing multiple options, including immunity, that could pave the way for it to get the information it wants from Meadows. A source familiar with the matter told CNN that among the 6,000 pages of documents Meadows has already provided to the committee are communications from January 6.

ranked-summaries/White House/Rank2.txt

@@ -0,0 +1 @@
+Mark Meadows will no longer cooperate with the House select committee, his attorney says. The committee says it will move forward with a scheduled deposition with Meadows on Wednesday. By proceeding with the deposition, the committee is setting up a path to hold Meadows in criminal contempt. The panel is weighing multiple options, including immunity, that could pave the way for it to get the information it wants from Meadows. A source familiar with the matter told CNN that among the 6,000 pages of documents Meadows has already provided to the committee are communications from January 6. That is still unclear who communicated with Meadows but the source said that "many people had Meadows' cell phone"

ranked-summaries/White House/Rank3.txt

@@ -0,0 +1 @@
+Former White House chief of staff Mark Meadows will no longer cooperate with the House select committee. The committee says it will move forward with a scheduled deposition with Meadows on Wednesday. By proceeding with the scheduled deposition, the committee is setting up a path to hold Meadows in criminal contempt. A source familiar with the matter told CNN that among the 6,000 pages of documents Meadows has already provided to the committee are communications from January 6. A Democratic member of the committee said Meadows' about-face is due in part to learning over the weekend that the committee had "issued wide ranging subpoenas for information from a third party communications provider".

requirements.txt

@@ -0,0 +1,10 @@
+beautifulsoup4==4.10.0
+streamlit==1.2.0
+transformers==4.15.0
+transformers-interpret==0.5.2
+sentence-transformers==2.2.0
+spacy==3.4.0
+spacy_streamlit==1.0.3
+###### en_core_web_lg @ https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.0.0/en_core_web_lg-3.0.0.tar.gz
+###### en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz
+en_core_web_lg @ https://huggingface.co/spacy/en_core_web_lg/resolve/main/en_core_web_lg-any-py3-none-any.whl

sample-articles-temp/biden.txt

@@ -0,0 +1,69 @@
+The White House says President Joe Biden told Russian President Vladimir Putin on Tuesday that the United States is prepared to launch strong economic measures should Russia invade Ukraine -- signaling that these new measures would pack a bigger punch than the sanctions issued in 2014 that failed to stop Russia from occupying Crimea.
+
+"I will look you in the eye and tell you, as President Biden looked Putin in the eye and told him today, that things we did not do in 2014 we are prepared to do now," national security adviser Jake Sullivan told reporters Tuesday afternoon after Biden's call with Putin.
+
+During the last several months, Russia has erected supply lines, including medical units and fuel, which could sustain a drawn-out conflict should Moscow choose to invade Ukraine, two sources familiar with the latest intelligence assessments told CNN. And recent US intelligence findings estimate Russia could begin a military offensive in Ukraine in a matter of months as it amasses up to 175,000 troops along the border. Sullivan said the administration still believes Putin has not made a decision on whether to to launch a military offensive against Ukraine.
+
+Sullivan did not go into specifics, but added that the US is coordinating with European allies "at a deep level of specificity."
+
+He said Biden "was direct and straightforward with President Putin, as he always is," later adding that "there was a lot of give and take. There was no finger-wagging, but the President was crystal clear about where the United States stands on all of these issues."
+
+Along with the economic ramifications mentioned on the call, Sullivan said Biden relayed that the US is prepared to bolster defense capabilities in the region.
+
+"He reiterated America's support for Ukraine's sovereignty and territorial integrity. He told President Putin directly that if Russia further invades Ukraine, the United States and our European allies would respond with strong economic measures," Sullivan said. "We would provide additional defensive materiel to the Ukrainians above and beyond that which we are already providing, and we would fortify our NATO allies on the eastern flank with additional capabilities in response to such an escalation."
+
+The other option, Sullivan said, is de-escalation and diplomacy.
+
+"The United States and our European allies would engage in a larger discussion that covers strategic issues, including our strategic concerns with Russia and Russia's strategic concerns. We managed to do this at the height of the Cold War and we developed mechanisms to help reduce instability and increase transparency," he said.
+
+A call in the Situation Room
+
+Biden and Putin spoke for about two hours over a secure video call earlier Tuesday.
+
+The two presidents tasked their respective teams with following up on their discussions, with the White House adding that "the US will do so in close coordination with allies and partners."
+
+In addition to the Ukraine issue, the two leaders discussed "the US-Russia dialogue on Strategic Stability, a separate dialogue on ransomware, as well as joint work on regional issues such as Iran," the White House said.
+
+A White House photograph from the meeting, which began at 10:07 a.m. ET and ended at 12:08 p.m. ET, showed that Secretary of State Antony Blinken, Sullivan and senior director for Russia and Central Asia Eric Green were seated next to Biden in the Situation Room during the video call.
+
+The two leaders took part in a summit in Geneva last June. Their last publicly known call was in July.
+
+Later Tuesday afternoon, Biden spoke with President Emmanuel Macron of France, Chancellor Angela Merkel of Germany, Prime Minister Mario Draghi of Italy and Prime Minister Boris Johnson of the United Kingdom -- the same group of European allies he conferred with Monday night. Tuesday marks Merkel's final full day in office.
+
+Biden briefed the allies on the Putin call and, according to a White House statement, the "leaders underscored their support for Ukraine's sovereignty and territorial integrity, as well as the need for Russia to reduce tensions and engage in diplomacy."
+
+Sullivan said during Tuesday's press briefing that the White House team was debriefing the embassies of NATO members, European Union members and key Indo-Pacific allies on the US-Russia call. Biden will speak with Ukrainian President Volodymyr Zelensky on Thursday.
+
+Biden is also expected to speak with the leaders of both houses of Congress to discuss "ways in which the administration and the Congress can work together on a bipartisan basis to stand up for American interests and values and stand behind our friends and partners," Sullivan added.
+
+The Russians said in their readout of the Biden-Putin meeting that Ukraine was the "predominant" subject of the conversation and Biden had "emphasized the allegedly 'threatening' nature of the movements of Russian troops near the Ukrainian borders and outlined sanctions measures that the United States and its allies would be ready to apply in the event of a further escalation of the situation."
+
+"In response, Vladimir Putin stressed that the responsibility should not be shifted onto the shoulders of Russia, since it is NATO that is making dangerous attempts to conquer Ukrainian territory and is building up its military potential at our borders," the Kremlin statement said. "Therefore, Russia is seriously interested in obtaining reliable, legally fixed guarantees excluding the expansion of NATO in the eastern direction and the deployment of offensive strike weapons systems in the states adjacent to Russia."
+
+Putin aide Yury Ushakov told reporters on a conference call Tuesday night that the Russian President told Biden while Americans are worried about Russian troops thousands of kilometers away from the United States, Moscow is "really worried about our security, the security of Russia in a global sense."
+
+Asked if Putin promised Biden that he would not use his troops in Ukraine, Ushakov dismissed the idea of an invasion.
+
+"There was not even such a discussion. What do you mean to bring in troops, what is it -- an invasion, or what? This was out of the question," Ushakov said.
+
+Measures under consideration
+
+US officials in recent days have weighed whether to issue wide-reaching sanctions on Russia meant to deter Putin from launching an invasion into Ukraine. They include new actions against members of Putin's inner circle and on Russian energy producers, and one potential "nuclear option" -- disconnecting Russia from the SWIFT international payment system used by banks around the world.
+
+The officials said final decisions hadn't been made on whether and when to apply the new sanctions, and said the Biden administration is currently in talks with European partners -- many of whom have closer economic relationships to Russia -- in the hopes of coordinating action.
+
+Along with considerations of economic sanctions, the administration is also exploring options for a potential evacuation of US citizens from Ukraine if Russia were to invade the country and create a dire security situation, half a dozen sources tell CNN. The contingency planning is being led by the Pentagon, the sources said, and comes as the administration briefs Congress on how the US is preparing. In a "gloomy" briefing to senators by senior State Department official Victoria Nuland on Monday night, Nuland outlined the tough sanctions package being prepared by the administration in response to a potential Russian attack but acknowledged that the US' options to deter an invasion are fairly limited, a person familiar with the briefing said.
+
+Nuland said on Tuesday that it is the US expectation that the controversial Nord Stream 2 pipeline "will be suspended" if Russia moves on Ukraine.
+
+But the White House relayed that while conversations are being had with Germany about the pipeline in the context of a potential invasion of Ukraine by Russia, taking action may not necessarily be the most effective deterrent against Russian aggression.
+
+The European Union "continues to fully support Ukraine in the face of the Russian aggression," European Commission President Ursula von der Leyen said in a tweet on Tuesday.
+
+"We will respond to any further aggressions, by scaling-up and expanding existing sanctions," she added.
+
+She also said the EU was "ready to take additional restrictive measures, in coordination with our partners."
+
+"The rise of extremism and autocracy can also be a security issue for countries. In this context, we must also speak about the Russian military movements and their massive build-up along Ukraine's eastern border," she continued.
+
+This story has been updated with additional developments Tuesday.

sample-articles-temp/numbers_to_text.txt

@@ -0,0 +1,6 @@
+4: White House
+9: Novak Djokovic
+11: Mark Levinson Airpods
+13: OnePlus 10 Pro
+16: Tencent Holdings
+

sample-articles-temp/protestors.txt

@@ -0,0 +1,25 @@
+Hundreds of protestors demonstrated at Boise State University in Idaho over the weekend following comments made at a conference by a professor who said men should be prioritized for fields of study such as engineering, medicine and law.
+
+BSU political science professor Scott Yenor made disparaging remarks about women and spoke critically of feminism at the National Conservatism Conference in Orlando, Florida, on October 31 in a speech titled "The Family Form that Nations Need."
+
+"Every effort made must be made not to recruit women into engineering, but rather to recruit and demand more of men who become engineers," Yenor said. "Ditto for med school, and the law, and every trade."
+
+CNN has reached out to Yenor for comment but has not heard back.
+
+In response to the attention the video received recently on social media, Yenor said on Twitter that "making special efforts to recruit women into fields where they don't seem to want to be" should be stopped, and he denied wanting to prevent women from obtaining those professions.
+
+He also posted a video in which he said the country should "elevate the importance of family life for both men and women in America."
+
+Boise State issued a statement saying it doesn't endorse Yenor's comments yet "cannot infringe" on his ability to make them.
+
+"Boise State University understands that the open exchange of ideas, which is fundamental to education, can introduce uncomfortable and even offensive ideas," the statement said. "However, the university cannot infringe upon the First Amendment rights of anyone in our community, regardless of whether we, as individual leaders, agree or disagree with the message. No single faculty member defines what Boise State -- or any public university -- endorses or stands for."
+
+Idaho state representative Brooke Green told CNN that Yenor's beliefs are "not only outdated, but completely sexist and reflect a society that no longer exists."
+
+Green helped organize the event Saturday at the university to counter Yenor's remarks, and said there were about 500 men and women in attendance.
+
+"Many young women at the university are worried their futures are in the hands of a tenured professor who believes they do not deserve to be there and occupy a seat that belongs to a man," Green said.
+
+"Women shouldn't have to spend time today defending our value in society or rights as human beings however, women wanted to gather to send a message saying we will continue to occupy professional spaces, whether it's a boardroom, courtroom, or leadership role within our community," she said.
+
+Boise State is the largest university in Idaho with nearly 20,000 students.

sample-articles/Mark Levinson Airpods.txt

@@ -0,0 +1,13 @@
+Not so long ago, the internet was debating what the folks at Apple were thinking charging $549 for the AirPods Max. At CES 2022, luxury audio brand Mark Levinson would like a word. The Harman-owned company (which also owns AKG, JBL and Harman Kardon, and is itself a Samsung subsidiary) mostly known for its high-end home and car audio systems has announced its first wireless headphones: the No. 5909. While they offer everything you'd expect from a premium set, they have a nearly $1,000 price tag that only a select few might commit to.
+
+The over-ear No. 5909 packs 40mm Beryllium drivers "expertly tuned to the Harman curve." The company explains that "the Harman curve" is acoustic response that it says has taken decades of research to construct. The result here is "incredible acoustic performance" in a set of "reference class" wireless headphones. Mark Levinson says that audio performance meets the guidelines for Hi-Res Audio certification thanks to 24-bit/96kHz signal processing and 40kHz acoustic response. The No. 5909 supports LDAC, AAC and aptX Adaptive wireless codecs via Bluetooth 5.1.
+
+Mark Levinson promises you'll hear details you haven't before, like "the slightest breath an artist takes" or "a hidden harmony." The company explains that the same "world-class sound engineers" that built the luxury brand's amps, turntables and streaming players are behind the tuning of the ultra pricey No. 5909.
+
+Mark Levinson/Harman
+
+Sound quality isn't the only consideration though. The No. 5909 has adaptive active noise cancellation (ANC) with three modes "for premium sound isolation" and an Ambient Aware feature that lets you tune into your surroundings as needed. The company also packed in four microphones for calls that are equipped with a so-called Smart Wind Adaption feature. The materials used to make the headphones are also better than the mostly plastic sets we typically see. The No. 5909 is built with an aluminum frame, painted metallic earcups, leather headband and replaceable leather ear cushions. An included hard shell travel case comes stocked with a USB-C charging cable, USB-C to USB-A adaptor, two USB-C to 3.5mm cables, 3.5mm to 6.3mm adaptor, airplane adaptor and a polishing cloth. Basically, it's everything you'd need to use the headphones on any setup — wired, wireless or while traveling.
+
+Mark Levinson says you can expect up to 30 hours of use with adaptive ANC active and up to 34 hours with the feature disabled. A quick-charge feature will give you up to six hours of play time in 15 minutes. Via an app for Android and iOS, you'll get some control over the headphones, but the company didn't go into specifics there.
+
+The No. 5909 will be available in black, pewter and red color options starting today for $999.

sample-articles/Novak Djokovic.txt

@@ -0,0 +1,48 @@
+Novak Djokovic has been granted permission to defend his Australian Open title
+
+Australians have reacted angrily to news that tennis player Novak Djokovic will play in the Australian Open, after being exempted from vaccination rules.
+
+All players and staff at the tournament must be vaccinated or have an exemption granted by an expert independent panel.
+
+Djokovic has not spoken about his vaccination status, but last year said he was "opposed to vaccination".
+
+Organisers say he has not been given special treatment. But Australians have criticised officials and the player.
+
+The controversy comes as the country is seeing tens of thousands of Covid-19 cases for the first time after enduring some of the world's strictest restrictions.
+
+Over 90% of Australia's over-16 population is fully vaccinated, but some Australians still cannot travel interstate or globally because of current restrictions.
+
+Amid the row, Australian Prime Minister Scott Morrison said Djokovic would be required to present evidence upon arrival that he has a genuine medical exemption from vaccination, or he would be "on the next plane home".
+
+"If that evidence is insufficient, then he won't be treated any different to anyone else and he'll be on the next plane home," the prime minister told reporters. "There should be no special rules for Novak Djokovic at all. None whatsoever."
+
+Many Australians had previously accused the government of allowing the rich and famous to do as they please while ordinary people remained separated from sick and dying loved ones.
+
+"I think it's a disgrace," Christine Wharton, who lives in Melbourne, where the Australian Open will be held, told ABC.
+
+"We've all done the right thing, we've all gone out and got our jabs and our boosters and we have someone that has come from overseas and all of a sudden he's been exempt and can play and I think it's an absolute disgrace and I won't be watching it."
+
+A&E doctor Stephen Parnis tweeted: "I don't care how good a tennis player he is. If he's refusing to get vaccinated, he shouldn't be allowed in. "If this exemption is true, it sends an appalling message to millions seeking to reduce #COVID19Aus risk to themselves & others."
+
+The decision raised eyebrows with some other tennis players too. "I just think it's very interesting. That's all I'm going to say," Australian Alex de Minaur said.
+
+Britain's Jamie Murray added: "I think if it was me that wasn't vaccinated I wouldn't be getting an exemption. You know, but well done to him for getting clear to come to Australia and compete."
+
+The Australian Open begins on 17 January, and the event's chief executive Craig Tiley said 26 athletes had applied for medical exemptions. "A handful" had been granted, he said, under guidelines set by federal regulators.
+
+"We made it extra difficult for anyone applying for an application to ensure it was the right process and to make sure the medical experts deal with it independently," he told Channel 9.
+
+Applications for medical exemptions are being assessed anonymously by two separate panels, with inflammatory cardiac illness or another acute condition listed as valid reasons.
+
+But it is also possible Djokovic has recently tested positive for the virus, which would allow him to defer taking the vaccine.
+
+He has not revealed his vaccination status and said last April: "Personally I am opposed to vaccination and I wouldn't want to be forced by someone to take a vaccine in order to be able to travel."
+
+On Tuesday he said on Instagram: "I've spent fantastic quality time with my loved ones over the break and today I'm heading down under with an exemption permission. Let's go 2022. I am ready to live and breathe tennis in the next few weeks of competition."
+
+Media caption,
+"Heartless" Queensland bars US couple from seeing dying father
+
+Victoria state government minister Jaala Pulford acknowledged the decision was "frustrating and upsetting", but also denied that Djokovic had received special treatment. Both she and Mr Tiley urged Djokovic to give more information to the public.
+
+"It'll certainly be helpful if Novak was to explain the conditions in which he's sought an exemption and granted an exemption but ultimately it's up to him," Mr Tiley said.

sample-articles/OnePlus 10 Pro.txt

@@ -0,0 +1,28 @@
+We're already seeing the effects of the Oppo merger on OnePlus' next flagship.
+Ron Amadeo - Jan 5, 2022 1:00 am UTC
+
+Enlarge / The OnePlus 10 Pro.
+
+OnePlus
+
+Official product news about the upcoming OnePlus 10 Pro has begun to trickle out. For now, we have an incomplete overview with some pictures and specs, while things like a price, release date, and the finer details will have to wait for later.
+
+First up: specs. OnePlus 10 Pro officially has the brand-new Qualcomm Snapdragon 8 Gen 1 SoC. This is Qualcomm's new flagship SoC for 2022, and it features a single ARM Cortex X2 core, three medium Cortex A710 CPUs, and four small Cortex A510 CPUs, all built on a 4 nm process. OnePlus isn't saying how much RAM and storage the 10 Pro has, but the 9 Pro came with 8GB or 12GB of RAM and 128GB or 256GB of storage. The company confirmed the display is 120 Hz but didn't give a size, though rumors say it's 6.7-inch, the same as the OnePlus 9 Pro. That fits the now-official dimensions, which are 163 × 73.9 × 8.55 mm.
+
+The battery is officially 5000 mAh, an upgrade over the 9 Pro's 4500 mAh battery. Considering the similar dimensions between the two phones, this is a welcome upgrade in battery density. OnePlus is also up to a whopping 80 W "SuperVOOC" quick charging now—an improvement over last year's 65 W "Warp Charge." OnePlus doesn't give any indication of what kind of charge time we can expect, but 65 W could charge the 9 Pro's 4500 mAh battery from 0-100 in a half-hour. Charging speed is still outpacing battery growth, so the 10 Pro should charge in under a half-hour. Just like last year, wireless charging is 50 W.
+
+Enlarge / Another look at that wacky camera block.
+
+OnePlus
+
+OnePlus has pitched itself as a scrappy startup in the past, but it's actually owned by the Chinese company BBK Electronics, one of the world's largest smartphone manufacturers. Just like General Motors, BBK has multiple brands (OnePlus, Oppo, Vivo, Realme, and iQOO) targeting different markets, and they share plenty of parts and engineering. While OnePlus and Oppo have always shared some engineering resources, last year it was announced OnePlus would actually be folded into Oppo.
+
+The Oppoization of OnePlus is going to be a major narrative for the OnePlus 10 Pro. We can already see a bit of it with the change from "Warp Charging" (OnePlus branding) to "SuperVOOC" (Oppo branding). But what really matters is the software, which will see OnePlus adopt Oppo's Color OS Android skin with a few custom tweaks rather than the separate codebases the two companies were running. We got a glimpse of this design direction via the OnePlus 9's Android 12 update, and the reviews were not kind. But we'll see what the first new phone software brings.
+
+As for the design, the camera block is really the only area where Android OEMs allow themselves to differentiate from the norm. This year, OnePlus is going with this square-ish design that wraps around the side of the phone. It looks a lot like the Galaxy S21 Ultra camera block, except that it's wrapped around the entire corner. Inside the camera block are three cameras and an LED flash. Right now, OnePlus is only disclosing megapixel counts, and those are 48MP, 50MP, and 8MP.
+
+Enlarge / This is not an official picture, but OnLeaks' clearly accurate leak from November is still our only look at the front of the phone.
+
+We don't actually have a picture of the front yet, so above is OnLeak's unofficial render from a few months ago. This has the camera hole on the left side instead of the middle. Other than that, it looks like every other Android phone on the market.
+
+It might be because of Oppo's influence, but OnePlus' launch is all sorts of weird this year. The phone is launching in China first on January 11. We don't have a price yet, but OnePlus' flagship prices have gone up every year so far, and the 9 Pro was $969. There's also no word on a US release date yet.