Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies

Authors:
Dragomir R. Radev;Hongyan Jing;Malgorzata Budzikowska
Affiliations:
University of Michigan, Ann Arbor, MI;Columbia University, New York, NY;IBM TJ Watson Research Center, Hawthorne, NY
Venue:
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
Year:
2000

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Abstract

We present a multi-document summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We also describe two new techniques, based on sentence utility and subsumption, which we have applied to the evaluation of both single and multiple document summaries. Finally, we describe two user studies that test our models of multi-document summarization.