Multi-document summarization by visualizing topical content

Authors:
Rie Kubota Ando;Branimir K. Boguraev;Roy J. Byrd;Mary S. Neff
Affiliations:
Cornell University, Ithaca, NY;IBM T.J. Watson Research Center, Hawthorne, NY;IBM T.J. Watson Research Center, Hawthorne, NY;IBM T.J. Watson Research Center, Hawthorne, NY
Venue:
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLPWorkshop on Automatic summarization - Volume 4
Year:
2000

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Abstract

This paper describes a framework for multi-document summarization which combines three premises: coherent themes can be identified reliably; highly representative themes, running across subsets of the document collection, can function as multi-document summary surrogates; and effective end-use of such themes should be facilitated by a visualization environment which clarifies the relationship between themes and documents. We present algorithms that formalize our framework, describe an implementation, and demonstrate a prototype system and interface.