docs: def tokenize_sentences(self, text, word_threshold=5):
          """
          Returns a list of sentences given an input string of text.
  
          :param text: input string
          :param word_threshold: number of significant words that a sentence must contain to be counted
          (to count all sentences set equal to 1; 5 by default)
          :return: list of sentences
          """
          punkt_params = PunktParameters()
          # Not using set literal to allow compatibility with Python 2.6
          punkt_params.abbrev_types = set([
              'dr', 'vs', 'mr', 'mrs', 'ms', 'prof', 'mt', 'inc', 'i.e', 'e.g'
          ])
          sentence_splitter = PunktSentenceTokenizer(punkt_params)
  
          # 1. TOKENIZE "UNPROCESSED" SENTENCES FOR DISPLAY
          # Need to adjust quotations for correct sentence splitting
          text_unprocessed = text.replace('?"', '? "').replace('!"', '! "').replace('."', '. "')
  
          # Treat line breaks as end of sentence (needed in cases where titles don't have a full stop)
          text_unprocessed = text_unprocessed.replace('\n', ' . ')
  
          # Perform sentence splitting
          unprocessed_sentences = sentence_splitter.tokenize(text_unprocessed)
  
          # Now that sentences have been split we can return them back to their normal formatting
          for ndx, sentence in enumerate(unprocessed_sentences):
              sentence = unicode_to_ascii(sentence)  # Sentence splitter returns unicode strings
              sentence = sentence.replace('? " ', '?" ').replace('! " ', '!" ').replace('. " ', '." ')
              sentence = self._remove_whitespace(sentence)  # Remove excess whitespace
              sentence = sentence[:-2] if (sentence.endswith(' .') or sentence.endswith(' . ')) else sentence
              unprocessed_sentences[ndx] = sentence
  
          # 2. PROCESS THE SENTENCES TO PERFORM STEMMING, STOPWORDS REMOVAL ETC. FOR MATRIX COMPUTATION
          processed_sentences = [self.sanitize_text(sen) for sen in unprocessed_sentences]
  
          # Sentences should contain at least 'word_threshold' significant terms
          filter_sentences = [i for i in range(len(processed_sentences))
                              if len(processed_sentences[i].replace('.', '').split(' ')) > word_threshold]
  
          processed_sentences = [processed_sentences[i] for i in filter_sentences]
          unprocessed_sentences = [unprocessed_sentences[i] for i in filter_sentences]
  
          return processed_sentences, unprocessed_sentences