code: def _intertext_score(full_text):
      '''returns tuple of scored sentences
         in order of appearance
         Note: Doing an A/B test to
         compare results, reverting to 
         original algorithm.'''
  
      sentences = sentence_tokenizer(full_text)
      norm = _normalize(sentences)
      similarity_matrix = pairwise_kernels(norm, metric='cosine')
      scores = _textrank(similarity_matrix)
      scored_sentences = []
      for i, s in enumerate(sentences):
          scored_sentences.append((scores[i],i,s))
      top_scorers = sorted(scored_sentences,
                           key=lambda tup: tup[0], 
                           reverse=True)
      return top_scorerf _intertext_score(full_text):
      '''returns tuple of scored sentences
         in order of appearance
         Note: Doing an A/B test to
         compare results, reverting to 
         original algorithm.'''
  
      sentences = sentence_tokenizer(full_text)
      norm = _normalize(sentences)
      similarity_matrix = pairwise_kernels(norm, metric='cosine')
      scores = _textrank(similarity_matrix)
      scored_sentences = []
      for i, s in enumerate(sentences):
          scored_sentences.append((scores[i],i,s))
      top_scorers = sorted(scored_sentences,
                           key=lambda tup: tup[0], 
                           reverse=True)
      return top_scorers
docs: returns tuple of scored sentences
         in order of appearance
         Note: Doing an A/B test to
         compare results, reverting to 
         original algorithm.