Automatic Text Summarization Using Unsupervised and Semi-supervised Learning

  • Massih-Reza Amini
  • Patrick Gallinari
Conference paper

DOI: 10.1007/3-540-44794-6_2

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2168)
Cite this paper as:
Amini MR., Gallinari P. (2001) Automatic Text Summarization Using Unsupervised and Semi-supervised Learning. In: De Raedt L., Siebes A. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2001. Lecture Notes in Computer Science, vol 2168. Springer, Berlin, Heidelberg

Abstract

This paper investigates a new approach for unsupervised and semisupervised learning. We show that this method is an instance of the Classification EM algorithm in the case of gaussian densities. Its originality is that it relies on a discriminant approach whereas classical methods for unsupervised and semi-supervised learning rely on density estimation. This idea is used to improve a generic document summarization system, it is evaluated on the Reuters news-wire corpus and compared to other strategies.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Massih-Reza Amini
    • 1
  • Patrick Gallinari
    • 1
  1. 1.LIP6University of Paris 6Paris cedex 05France

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