A Comparative Study of the Impact of Statistical and Semantic Features in the Framework of Extractive Text Summarization

  • Tatiana Vodolazova
  • Elena Lloret
  • Rafael Muñoz
  • Manuel Palomar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7499)


This paper evaluates the impact of a set of statistical and semantic features as applied to the task of extractive summary generation for English. This set includes word frequency, inverse sentence frequency, inverse term frequency, corpus-tailored stopwords, word senses, resolved anaphora and textual entailment. The obtained results show that not all of the selected features equally benefit the performance. The term frequency combined with stopwords filtering is a highly competitive baseline that nevertheless can be topped when semantic information is included. However, in the selected experiment environment the recall values improved less than expected and we are interested in further investigating the reasons.


Semantic Feature Term Frequency Word Sense Disambiguation Text Summarization Anaphora Resolution 
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 2012

Authors and Affiliations

  • Tatiana Vodolazova
    • 1
  • Elena Lloret
    • 1
  • Rafael Muñoz
    • 1
  • Manuel Palomar
    • 1
  1. 1.Dept. Lenguajes y Sistemas InformáticosUniversidad de AlicanteAlicanteSpain

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