code: def summarize(self, text, length=5, weighting='frequency', norm=None):
          
  
          text = self._parse_input(text)
  
          sentences, unprocessed_sentences = self._tokenizer.tokenize_sentences(text)
  
          length = self._parse_summary_length(length, len(sentences))
          if length == len(sentences):
              return unprocessed_sentences
  
          # Compute the word frequency matrix. If norm is set to 'l1' or 'l2' then words are normalized
          # by the length of their associated sentences (such that each vector of sentence terms sums to 1).
          word_matrix = self._compute_matrix(sentences, weighting=weighting, norm=norm)
  
          # Build the similarity graph by calculating the number of overlapping words between all
          # combinations of sentences.
          similarity_matrix = (word_matrix * word_matrix.T)
  
          similarity_graph = networkx.from_scipy_sparse_matrix(similarity_matrix)
          scores = networkx.pagerank(similarity_graph)
  
          ranked_sentences = sorted(
              ((score, ndx) for ndx, score in scores.items()), reverse=True
          )
  
          top_sentences = [ranked_sentences[i][1] for i in range(length)]
          top_sentences.sort()
  
          return [unprocessed_sentences[i] for i in top_sentences]
docs: Implements the TextRank summarization algorithm, which follows closely to the PageRank algorithm for ranking
          web pages.
  
          :param text: a string of text to be summarized, path to a text file, or URL starting with http
          :param length: the length of the output summary; either a number of sentences (e.g. 5) or a percentage
          of the original document (e.g. 0.5)
          :param weighting: 'frequency', 'binary' or 'tfidf' weighting of sentence terms ('frequency' by default)
          :param norm: if 'l1' or 'l2', normalizes words by the length of their associated sentence to "down-weight"
          the voting power of long sentences (None by default)
          :return: list of sentences for the summary