Text-Mining Research Group

A Practical Approach to Automatic Text Summarization

The significance of automatic document summarization increases with the threat of informationoverload we are facing. Short summaries can be presented to users, for example, in place of fulllengthdocuments found by a search engine in response to a user’s query. We have analyzed variousapproaches to document summarization, using some existing algorithms and combining these with anovel use of itemsets. The resulting summarizer is evaluated by comparing classification of originaldocuments and that of abstracts generated automatically. Despite highly promising results achieved bythis evaluation, readability of abstracts must be further improved by integrating additional heuristicapproaches.

Keywords: document summarization, summarizer, condensation, abstract, abstracting, extraction, text, machine learning, classification, categorization, sentence selection, highlight, classifier, heuristics, itemsets, term frequency, evaluation

Year: 2003

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Jiří Hynek

Phone: +420 603492837
E-mail: jhynek@kiv.zcu.cz
WWW: http://www.kiv.zcu.cz/staff/osobni.php?id_osoby=147&lang=EN

Jiri, a co-founder of the Text-Mining Research Group, works as a lecturer at the Dept. of Computer Science and Engineering. His research interests include machine learning and language-related problems. Jiri’s teaching activity is focused on good writing style and technical writing in general.

Karel Ježek

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.

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).