Text Summarization and Singular Value Decomposition

Text Summarization and Singular Value Decomposition

In this paper we present the usage of singular value decomposition (SVD) in text summarization. Firstly, we mention the taxonomy of generic text summarization methods. Then we describe principles of the SVD and its possibilities to identify semantically important parts of a text. We propose a modification of the SVD-based summarization, which improves the quality of generated extracts. In the second part we propose two new evaluation methods based on SVD, which measure content similarity between an original document and its summary. In evaluation part, our summarization approach is compared with 5 other available summarizers. For evaluation of a summary quality we used, apart from a classical content-based evaluator, both newly developed SVD-based evaluators. Finally, we study the influence of the summary length on its quality from the angle of the three evaluation methods mentioned.

Keywords: Text Summarization, Summarization Evaluation, Singular Value Decomposition

Year: 2004

Journal ISSN: 0302-9743View record in Web of Science®

Authors of this publication:

Josef Steinberger

E-mail: jstein@kiv.zcu.cz

Josef is an associated professor at the Department of computer science and engineering at the University of West Bohemia in Pilsen, Czech Republic. He is interested in media monitoring and analysis, mainly automatic text summarisation, sentiment analysis and coreference resolution.

Karel Je┼żek

Phone:  +420 377632475
E-mail: jezek_ka@kiv.zcu.cz
WWW: https://cs.wikipedia.org/wiki/Karel_Je%C5%BEek_(informatik)

Karel is the former group coordinator and a supervisor of PhD students working at research projects of this Group.

Related Projects:


Automatic Text Summarisation

Authors:  Josef Steinberger, Karel Je┼żek, Michal Campr, Ji┼Ö├ş Hynek
Desc.:Automatic text summarisation using various text mining methods, mainly Latent Semantic Analysis (LSA).