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A Method for the Automatic Summarization of Topic-Based Clusters of Documents

  • Aurora Pons-Porrata
  • José Ruiz-Shulcloper
  • Rafael Berlanga-Llavori
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

In this paper we propose an effective method to summarize document clusters. This method is based on the Testor Theory, and it is applied to a group of newspaper articles in order to summarize the events that they describe. This method is also applicable to either a very large document collection or a very large document, in order to identify the main themes (topics) of the collection (documents) and to summarize them. The results obtained in the experiments demonstrate the usefulness of the proposed method.

Keywords

Typical Testors Compression Rate Testor Theory Newspaper Article Document Cluster 
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 2003

Authors and Affiliations

  • Aurora Pons-Porrata
    • 1
  • José Ruiz-Shulcloper
    • 2
  • Rafael Berlanga-Llavori
    • 3
  1. 1.Universidad de OrienteSantiago de CubaCuba
  2. 2.Institute of Cybernetics, Mathematics and PhysicsLa HabanaCuba
  3. 3.Universitat Jaume ICastellónSpain

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