A new hybrid summarizer based on vector space model, statistical physics and linguistics

  • Authors:
  • Iria Da Cunha;Silvia Fernández;Patricia Velázquez Morales;Jorge Vivaldi;Eric SanJuan;Juan Manuel Torres-Moreno

  • Affiliations:
  • Institute for Applied Linguistics, Universitat Pompeu Fabra, Barcelona, España;Laboratoire Informatique d'Avignon, Avignon Cedex 9, France and Laboratoire de Physique des Matériaux, CNRS, UMR, Nancy, France;-;Institute for Applied Linguistics, Universitat Pompeu Fabra, Barcelona, España;Laboratoire Informatique d'Avignon, Avignon Cedex 9, France;Laboratoire Informatique d'Avignon, Avignon Cedex 9, France and École Polytechnique de Montréal, DGI, Montréal, Québec, Canada

  • Venue:
  • MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
  • Year:
  • 2007

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Abstract

In this article we present a hybrid approach for automatic summarization of Spanish medical texts. There are a lot of systems for automatic summarization using statistics or linguistics, but only a few of them combining both techniques. Our idea is that to reach a good summary we need to use linguistic aspects of texts, but as well we should benefit of the advantages of statistical techniques. We have integrated the Cortex (Vector Space Model) and Enertex (statistical physics) systems coupled with the Yate term extractor, and the Disicosum system (linguistics). We have compared these systems and afterwards we have integrated them in a hybrid approach. Finally, we have applied this hybrid system over a corpora of medical articles and we have evaluated their performances obtaining good results.