class_docstr: SumBasic: a frequency-based summarization system that adjusts word frequencies as
  sentences are extracted.
  Source: http://www.cis.upenn.edu/~nenkova/papers/ipm.pdf
class_docstr_tok: ["SumBasic",":","a","frequency-based","summarization","system","that","adjusts","word","frequencies","as","sentences","are","extracted",".","Source",":","http",":","\/\/www.cis.upenn.edu\/~nenkova\/papers\/ipm.pdf"]
docstr: Computes the normalized term frequency as explained in http://www.tfidf.com/
docstr_tok: ["Computes","the","normalized","term","frequency","as","explained","in","http",":","\/\/www.tfidf.com\/"]
code: def _compute_tf(self, sentences):
      """
      Computes the normalized term frequency as explained in http://www.tfidf.com/
      """
      content_words = self._get_all_content_words_in_doc(sentences)
      content_words_count = len(content_words)
      content_words_freq = self._compute_word_freq(content_words)
      content_word_tf = dict(
          (k, v / content_words_count)
          for (k, v) in content_words_freq.items()
      )
      return content_word_tf
  
file_path: sumy/sumy/summarizers/sum_basic.py
imports: from __future__ import absolute_import
  from __future__ import division, print_function, unicode_literals
  from ._summarizer import AbstractSummarizer
label_desc: Use image node_id 11 for calling the SumBasicSummarizer obj's underlying member method code with example usage: obj._compute_tf(sentences) and returns: content_word_tf