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Multi-document Automatic Text Summarization Using Entropy Estimates

  • G. Ravindra
  • N. Balakrishnan
  • K. R. Ramakrishnan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2932)

Abstract

This paper describes a sentence ranking technique using entropy measures, in a multi-document unstructured text summarization application. The method is topic specific and makes use of a simple language independent training framework to calculate entropies of symbol units. The document set is summarized by assigning entropy-based scores to a reduced set of sentences obtained using a graph representation for sentence similarity. The performance is seen to be better than some of the common statistical techniques, when applied on the same data set. Commonly used measures like precision, recall and f-score have been modified and used as a new set of measures for comparing the performance of summarizers. The rationale behind such a modification is also presented. Experimental results are presented to illustrate the relevance of this method in cases where it is difficult to have language specific dictionaries, translators and document-summary pairs for training.

Keywords

Singular Value Decomposition Latent Semantic Analysis Membership Grade Latent Semantic Indexing Entropy Estimate 
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 2004

Authors and Affiliations

  • G. Ravindra
    • 1
  • N. Balakrishnan
    • 1
  • K. R. Ramakrishnan
    • 2
  1. 1.Institute of ScienceSupercomputer Education and Research CenterBangaloreIndia
  2. 2.Dept. of Electrical Engineering, Institute of ScienceBangaloreIndia

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