Text Summarization by Sentence Extraction Using Unsupervised Learning

  • René Arnulfo García-Hernández
  • Romyna Montiel
  • Yulia Ledeneva
  • Eréndira Rendón
  • Alexander Gelbukh
  • Rafael Cruz
Conference paper

DOI: 10.1007/978-3-540-88636-5_12

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5317)
Cite this paper as:
García-Hernández R.A., Montiel R., Ledeneva Y., Rendón E., Gelbukh A., Cruz R. (2008) Text Summarization by Sentence Extraction Using Unsupervised Learning. In: Gelbukh A., Morales E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science, vol 5317. Springer, Berlin, Heidelberg

Abstract

The main problem for generating an extractive automatic text summary is to detect the most relevant information in the source document. Although, some approaches claim being domain and language independent, they use high dependence knowledge like key-phrases or golden samples for machine-learning approaches. In this work, we propose a language- and domain-independent automatic text summarization approach by sentence extraction using an unsupervised learning algorithm. Our hypothesis is that an unsupervised algorithm can help for clustering similar ideas (sentences). Then, for composing the summary, the most representative sentence is selected from each cluster. Several experiments in the standard DUC-2002 collection show that the proposed method obtains more favorable results than other approaches.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • René Arnulfo García-Hernández
    • 1
  • Romyna Montiel
    • 1
  • Yulia Ledeneva
    • 1
  • Eréndira Rendón
    • 1
  • Alexander Gelbukh
    • 1
  • Rafael Cruz
    • 1
  1. 1.Pattern Recognition Laboratory, Toluca Institute of Technology, Mexico, Autonomous University of the State of Mexico, Mexico, Center for Computing Research, National Polytechnic Institute, Mexico, SoNet RC, University of Center Europe in SkalicaSlovakia

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