Learning predicate insertion rules for document abstracting

  • Authors:
  • Horacio Saggion

  • Affiliations:
  • TALN, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain

  • Venue:
  • CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
  • Year:
  • 2011

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Abstract

The insertion of linguistic material into document sentences to create new sentences is a common activity in document abstracting. We investigate a transformation-based learning method to simulate this type of operation relevant for text summarization. Our work is framed on a theory of transformation-based abstracting where an initial text summary is transformed into an abstract by the application of a number of rules learnt from a corpus of examples. Our results are as good as recent work on classification-based predicate insertion.