Automatic Text Summarization (The state of the art 2007 and new challenges)

Automatic Text Summarization (The state of the art 2007 and new challenges)

The headline of this paper names a research area originating from the late 50’s but not loosing its popularity until the present time. Moreover, one of the most relevant today’s problems caused by the rapid growth of the Web, which is called information overloading, has increased the necessity of more sophisticated and powerful summarizers. This paper shortly introduces a taxonomy of summarization methods and an overview of their principles from classical ones, over corpus based, to knowledge rich approaches. We consider various aspects which can affect their classification. A special attention is devoted to application of recent information reduction methods, based on algebraic transformations. Further, we introduce experiences with the development of our own summarizing method. Finally, some new ideas and a conception for the future of this field are mentioned.

Keywords: Automatic text summarization, summary evaluation, latent semantic analysis

Year: 2008

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Authors of this publication:

Karel Ježek

Phone:  +420 377632475, 377632400

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

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.

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).