A New Hybrid Summarizer Based on Vector Space Model, Statistical Physics and Linguistics

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4827)


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.


Hybrid System Vector Space Model Term Candidate Elimination Rule Term Extraction 
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 2007

Authors and Affiliations

  1. 1.Institute for Applied Linguistics, Universitat Pompeu Fabra, Barcelona, Espan̈a 
  2. 2.Laboratoire Informatique d’Avignon, BP1228, 84911 Avignon Cedex 9France
  3. 3.École Polytechnique de Montréal/DGI, Montréal (Québec)Canada
  4. 4.Laboratoire de Physique des Matériaux, CNRS UMR 7556, NancyFrance

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