Enhancing N-Gram-based Summary Evaluation Using Information Content and a Taxonomy

Enhancing N-Gram-based Summary Evaluation Using Information Content and a Taxonomy

In this paper we propose a novel information-theoretic metricfor automatic summary evaluation when model summaries are availableas in the setting of the AESOP task of the Update Summarization trackof the Text Analysis Conference (TAC). The metric is based on theconcept of information content operationalized by using a taxonomy.Hereby, we present and discuss the results obtained at TAC 2009.

Keywords: Summary Evaluation

Year: 2010

Journal ISSN: 0302-9743
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Authors of this publication:


Mijail Alexandrov Kabadjov



Josef Steinberger


E-mail: jstein@kiv.zcu.cz

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.

Ralf Steinberger



Massimo Poesio



Bruno Pouliquen



Related Projects:


Project

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).