Trimming CFG parse trees for sentence compression using machine learning approaches

  • Authors:

  • Yuya Unno;Takashi Ninomiya;Yusuke Miyao;Jun'ichi Tsujii

  • Affiliations:

  • University of Tokyo;University of Tokyo;University of Tokyo;University of Tokyo and University of Manchester and SORST, JST, Tokyo, Japan

  • Venue:
  • COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
  • Year:
  • 2006

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Abstract

Sentence compression is a task of creating a short grammatical sentence by removing extraneous words or phrases from an original sentence while preserving its meaning. Existing methods learn statistics on trimming context-free grammar (CFG) rules. However, these methods sometimes eliminate the original meaning by incorrectly removing important parts of sentences, because trimming probabilities only depend on parents' and daughters' non-terminals in applied CFG rules. We apply a maximum entropy model to the above method. Our method can easily include various features, for example, other parts of a parse tree or words the sentences contain. We evaluated the method using manually compressed sentences and human judgments. We found that our method produced more grammatical and informative compressed sentences than other methods.