Aspect-Driven News Summarization

Aspect-Driven News Summarization

A summary of any event type is only complete if certain in-formation aspects are mentioned. For a court trial, readers will at leastwant to know who is involved and what the charges and the sentence are.For a natural disaster, they will ask for the disaster type, the victims andother damages. Will a co-occurrence or frequency-based sentence extrac-tion summariser automatically provide the requested information, or arethe results better if an information extraction (IE) system rst detectsthe summary-crucial aspects? To answer this question, we compared theperformance of a purely co-occurrence-based method with a system thatadditionally makes use of targeted IE. As each event type requires di er-ent information aspects and not all of them were covered by the existingIE software, we used a tool that learns semantically related terms to coverthe remaining aspects. The comprehensive evaluation in the TAC'2010competition showed that event extraction is indeed bene cial for sum-marisation performance, and that summary quality is directly related toIE quality. Our integrated system was ranked among the top systemsparticipating at TAC.

Year: 2011

Journal ISSN: 0976-0962
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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.

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).