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Evaluating Syntactic Sentence Compression for Text Summarisation

  • Prasad Perera
  • Leila Kosseim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7934)

Abstract

This paper presents our work on the evaluation of syntactic based sentence compression for automatic text summarization. Sentence compression techniques can contribute to text summarization by removing redundant and irrelevant information and allowing more space for more relevant content. However, very little work has focused on evaluating the contribution of this idea for summarization. In this paper, we focus on pruning individual sentences in extractive summaries using phrase structure grammar representations. We have implemented several syntax-based pruning techniques and evaluated them in the context of automatic summarization, using standard evaluation metrics. We have performed our evaluation on the TAC and DUC corpora using the BlogSum and MEAD summarizers. The results show that sentence pruning can achieve compression rates as low as 60%, however when using this extra space to fill in more sentences, ROUGE scores do not improve significantly.

Keywords

Noun Phrase Relative Clause Compression Rate Prepositional Phrase Pruning Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Prasad Perera
    • 1
  • Leila Kosseim
    • 1
  1. 1.Dept. of Computer Science & Software EngineeringConcordia University MontrealCanada

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