Using Latent Semantic Analysis in Text Summarization and Summary Evaluation

Using Latent Semantic Analysis in Text Summarization and Summary Evaluation

This paper deals with using latent semantic analysis in text summarization. We describe a generic text summarization method which uses the latent semantic analysis (LSA) technique to identify semantically important sentences. This method has been further improved. Then we propose two new evaluation methods based on LSA, which measure content similarity between an original document and its summary. In the evaluation part we compare seven summarizers by a classical content-based evaluator and by the two new LSA evaluators. We also study an influence of summary length on its quality from the angle of the three mentioned evaluation methods.

Keywords: Generic Text Summarization, Latent Semantic Analysis, Summary Evaluation

Year: 2004

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Authors of this publication:

Josef Steinberger


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

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

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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).