text: #tutorial : https://towardsdatascience.com/understand-text-summarization-and-create-your-own-summarizer-in-python-b26a9f09fc70
  #!/usr/bin/env python
  # coding: utf-8
  import nltk
  from nltk.corpus import stopwords
  from nltk.cluster.util import cosine_distance
  import numpy as np
  import networkx as nx
   
  def read_article(file_name):
      file = open(file_name, "r")
      filedata = file.readlines()
      article = filedata[0].split(". ")
      sentences = []
  
      for sentence in article:
          print(sentence)
          sentences.append(sentence.replace("[^a-zA-Z]", " ").split(" "))
      sentences.pop() 
      
      return sentences
  
  def sentence_similarity(sent1, sent2, stopwords=None):
      if stopwords is None:
          stopwords = []
   
      sent1 = [w.lower() for w in sent1]
      sent2 = [w.lower() for w in sent2]
   
      all_words = list(set(sent1 + sent2))
   
      vector1 = [0] * len(all_words)
      vector2 = [0] * len(all_words)
   
      # build the vector for the first sentence
      for w in sent1:
          if w in stopwords:
              continue
          vector1[all_words.index(w)] += 1
   
      # build the vector for the second sentence
      for w in sent2:
          if w in stopwords:
              continue
          vector2[all_words.index(w)] += 1
   
      return 1 - cosine_distance(vector1, vector2)
   
  def build_similarity_matrix(sentences, stop_words):
      # Create an empty similarity matrix
      similarity_matrix = np.zeros((len(sentences), len(sentences)))
   
      for idx1 in range(len(sentences)):
          for idx2 in range(len(sentences)):
              if idx1 == idx2: #ignore if both are same sentences
                  continue 
              similarity_matrix[idx1][idx2] = sentence_similarity(sentences[idx1], sentences[idx2], stop_words)
  
      return similarity_matrix
  
  
  def generate_summary(file_name, top_n=5):
      nltk.download("stopwords")
      stop_words = stopwords.words('english')
      summarize_text = []
  
      # Step 1 - Read text anc split it
      sentences =  read_article(file_name)
  
      # Step 2 - Generate Similary Martix across sentences
      sentence_similarity_martix = build_similarity_matrix(sentences, stop_words)
  
      # Step 3 - Rank sentences in similarity martix
      sentence_similarity_graph = nx.from_numpy_array(sentence_similarity_martix)
      scores = nx.pagerank(sentence_similarity_graph)
  
      # Step 4 - Sort the rank and pick top sentences
      ranked_sentence = sorted(((scores[i],s) for i,s in enumerate(sentences)), reverse=True)    
      print("Indexes of top ranked_sentence order are ", ranked_sentence)    
  
      for i in range(top_n):
        summarize_text.append(" ".join(ranked_sentence[i][1]))
  
      # Step 5 - Offcourse, output the summarize texr
      print("Summarize Text: \n", ". ".join(summarize_text))
  
  # let's begin
  generate_summary( "msft.txt", 2)
  