code: def _get_significant_words(self, words):
      words = map(self.normalize_word, words)
      words = tuple(
          self.stem_word(w) for w in words if w not in self._stop_words
      )
  
      model = TfDocumentModel(words)
  
      # take only best `significant_percentage` % words
      best_words_count = int(len(words) * self.significant_percentage)
      words = model.most_frequent_terms(best_words_count)
  
      # take only words contained multiple times in document
      return tuple(t for t in words if model.term_frequency(t) > 1)
  
file_path: sumy/sumy/summarizers/luhn.py
imports: from __future__ import absolute_import
  from __future__ import division, print_function, unicode_literals
  from ..models import TfDocumentModel
  from ._summarizer import AbstractSummarizer
label_desc: Use image node_id 4 for calling the LuhnSummarizer obj's underlying member method code with example usage: obj._get_significant_words(words) and returns: tuple