A hybrid statistical/linguistic model for generating news story gists

Authors:
William P. Doran;Nicola Stokes;Eamonn Newman;John Dunnion;Joe Carthy
Affiliations:
University College Dublin, Ireland;University College Dublin, Ireland;University College Dublin, Ireland;University College Dublin, Ireland;University College Dublin, Ireland
Venue:
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2004

Citing 1
Cited 1

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NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1

Storyline-based summarization for news topic retrospection

Decision Support Systems

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Abstract

In this paper, we describe a News Story Gisting system that generates a 10-word short summary of a news story. This system uses a machine learning technique to combine linguistic, statistical and positional information in order to generate an appropriate summary. We also present the results of an automatic evaluation of this system with respect to the performance of other baseline summarisers using the new ROUGE evaluation metric.