SUTLER: Update Summarizer Based on Latent Topics

SUTLER: Update Summarizer Based on Latent Topics

This paper deals with our past and recent research in text summarization. We went from single-document summarization through multi-document summarization to update summarization. We describe the development of our summarizer which is based on latent semantic analysis (LSA). The classical LSA-based summarization model was improved by Iterative Residual Rescaling. We propose the update summarization component which determines the redundancy and novelty of each topic discovered by LSA. Moreover, we have modified the sentence selection component in order to prevent inner summary redundancy. The results of our first participation in TAC/DUC evaluation seem to be promising.

Keywords: Multi-Document Summarization, Update Summarization, Summarization Evaluation, Text Analysis Conference

Year: 2009

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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 Ježek

Phone:  +420 377632475

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 Ježek, Michal Campr, Jiří Hynek
Desc.:Automatic text summarisation using various text mining methods, mainly Latent Semantic Analysis (LSA).