summarizer.py
def summarizer(text):
    '''Summarizes text by tokenizing, creating a word frequency list, 
        finding sentence scores, and then selecting sentences with 
        highest sentence scores'''

    stopwords = list(STOP_WORDS)
    #print(stopwords)

    # Loading model for tokenization
    nlp = spacy.load('en_core_web_sm')

    # Tokenizing text with spacy
    doc = nlp(text)

    tokens = [token.text for token in doc]
    #print(tokens)

    # Finding Word Frequencies
    word_frequencies = {}

    for word in doc:
        if word.text.lower() not in stopwords:
            if word.text.lower() not in punctuation:
                if word.text.lower() not in word_frequencies.keys():
                    # Adding new word to word_frequency
                    word_frequencies[word.text.lower()] = 1
                else:
                    # Incrementing frequency in word already exists
                    word_frequencies[word.text.lower()] += 1

    #print(word_frequencies)

    # Normalizing Word Frequencies
    max_frequency = max(word_frequencies.values())
    #print(max_frequency)

    for word in word_frequencies.keys():
        word_frequencies[word] /= max_frequency

    #print(word_frequencies)

    # Sentence Tokenization
    sentence_tokens = [sent for sent in doc.sents]
    #print(sentence_tokens)

    # Calculating sentence scores
    sentence_scores = {}

    for sent in sentence_tokens:
        for word in sent:
            if word.text.lower() in word_frequencies.keys():
                if sent not in sentence_scores.keys():
                    sentence_scores[sent] = word_frequencies[word.text.lower()]
                else:
                    sentence_scores[sent] += word_frequencies[word.text.lower()]

    #print(sentence_scores)

    # Getting Sentences with highest scores
    sentences_percent = 0.2
    sentences_selected = int(len(sentence_tokens)*sentences_percent)
    #print(sentences_selected)

    #heapq.nlargest(selectCount, iterable, keys )
    summary_sentences = nlargest(sentences_selected, sentence_scores, key = sentence_scores.get)
    #print(summary_sentences)
    summary_sentences = [word.text for word in summary_sentences]
    summary = " ".join(summary_sentences)
    return summary
Summarizes text by tokenizing, creating a word frequency list, 
        finding sentence scores, and then selecting sentences with 
        highest sentence scores
Summarizes text by tokenizing, creating a word frequency list,
finding sentence scores, and then selecting sentences with
highest sentence scores
Summarizes text by tokenizing creating a word frequency list finding sentence scores and then selecting sentences with highest sentence scores
def summarizer(text):
    stopwords = list(STOP_WORDS)
    nlp = spacy.load('en_core_web_sm')
    doc = nlp(text)
    tokens = [token.text for token in doc]
    word_frequencies = {}
    for word in doc:
        if word.text.lower() not in stopwords:
            if word.text.lower() not in punctuation:
                if word.text.lower() not in word_frequencies.keys():
                    word_frequencies[word.text.lower()] = 1
                else:
                    word_frequencies[word.text.lower()] += 1
    max_frequency = max(word_frequencies.values())
    for word in word_frequencies.keys():
        word_frequencies[word] /= max_frequency
    sentence_tokens = [sent for sent in doc.sents]
    sentence_scores = {}
    for sent in sentence_tokens:
        for word in sent:
            if word.text.lower() in word_frequencies.keys():
                if sent not in sentence_scores.keys():
                    sentence_scores[sent] = word_frequencies[word.text.lower()]
                else:
                    sentence_scores[sent] += word_frequencies[word.text.lower()]
    sentences_percent = 0.2
    sentences_selected = int(len(sentence_tokens)*sentences_percent)
    summary_sentences = nlargest(sentences_selected, sentence_scores, key = sentence_scores.get)
    summary_sentences = [word.text for word in summary_sentences]
    summary = " ".join(summary_sentences)
    return summary
def summarizer ( text ) : stopwords = list ( STOP_WORDS ) nlp = spacy . load ( 'en_core_web_sm' ) doc = nlp ( text ) tokens = [ token . text for token in doc ] word_frequencies = { } for word in doc : if word . text . lower ( ) not in stopwords : if word . text . lower ( ) not in punctuation : if word . text . lower ( ) not in word_frequencies . keys ( ) : word_frequencies [ word . text . lower ( ) ] = 1 else : word_frequencies [ word . text . lower ( ) ] += 1 max_frequency = max ( word_frequencies . values ( ) ) for word in word_frequencies . keys ( ) : word_frequencies [ word ] /= max_frequency sentence_tokens = [ sent for sent in doc . sents ] sentence_scores = { } for sent in sentence_tokens : for word in sent : if word . text . lower ( ) in word_frequencies . keys ( ) : if sent not in sentence_scores . keys ( ) : sentence_scores [ sent ] = word_frequencies [ word . text . lower ( ) ] else : sentence_scores [ sent ] += word_frequencies [ word . text . lower ( ) ] sentences_percent = 0.2 sentences_selected = int ( len ( sentence_tokens ) * sentences_percent ) summary_sentences = nlargest ( sentences_selected , sentence_scores , key = sentence_scores . get ) summary_sentences = [ word . text for word in summary_sentences ] summary = " " . join ( summary_sentences ) return summary
Summarizes text by tokenizing, creating a word frequency list,
finding sentence scores, and then selecting sentences with
highest sentence scores
Summarizes text by tokenizing creating a word frequency list finding sentence scores and then selecting sentences with highest sentence scores
'''Summarizes text by tokenizing, creating a word frequency list, 
        finding sentence scores, and then selecting sentences with 
        highest sentence scores''' #print(stopwords) # Loading model for tokenization # Tokenizing text with spacy #print(tokens) # Finding Word Frequencies # Adding new word to word_frequency # Incrementing frequency in word already exists #print(word_frequencies) # Normalizing Word Frequencies #print(max_frequency) #print(word_frequencies) # Sentence Tokenization #print(sentence_tokens) # Calculating sentence scores #print(sentence_scores) # Getting Sentences with highest scores #print(sentences_selected) #heapq.nlargest(selectCount, iterable, keys ) #print(summary_sentences)
text