Multi-Document Automatic Text Summarization Using Entropy Estimates

Ravindra, G and Balakrishnan, N and Ramakrishnan, KR (2004) Multi-Document Automatic Text Summarization Using Entropy Estimates. [Book Chapter]

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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.

Item Type:	Book Chapter
Publication:	SOFSEM 2004: Theory and Practice of Computer Science (Lecture Notes in Computer Science)
Series.:	Lecture Notes in Computer Science
Publisher:	Springer-Verlag Heidel berg
Additional Information:	Copyright for this article belongs to Springer-Verlag.
Keywords:	text summarization;collocation information;fuzzy f-score
Department/Centre:	Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited:	09 Jun 2005
Last Modified:	19 Sep 2010 04:19
URI:	http://eprints.iisc.ac.in/id/eprint/3278

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