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Visualization-enabled multi-document summarization by Iterative Residual Rescaling

Published online by Cambridge University Press:  28 February 2005

RIE ANDO
Affiliation:
IBM T. J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA e-mail: rie1@us.ibm.com, bran@us.ibm.com, roybyrd@us.ibm.com, maryneff@us.ibm.com
BRANIMIR BOGURAEV
Affiliation:
IBM T. J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA e-mail: rie1@us.ibm.com, bran@us.ibm.com, roybyrd@us.ibm.com, maryneff@us.ibm.com
ROY BYRD
Affiliation:
IBM T. J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA e-mail: rie1@us.ibm.com, bran@us.ibm.com, roybyrd@us.ibm.com, maryneff@us.ibm.com
MARY NEFF
Affiliation:
IBM T. J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA e-mail: rie1@us.ibm.com, bran@us.ibm.com, roybyrd@us.ibm.com, maryneff@us.ibm.com

Abstract

This paper describes a novel approach to multi-document summarization, which explicitly addresses the problem of detecting, and retaining for the summary, multiple themes in document collections. We place equal emphasis on the processes of theme identification and theme presentation. For the former, we apply Iterative Residual Rescaling (IRR); for the latter, we argue for graphical display elements. IRR is an algorithm designed to account for correlations between words and to construct multi-dimensional topical space indicative of relationships among linguistic objects (documents, phrases, and sentences). Summaries are composed of objects with certain properties, derived by exploiting the many-to-many relationships in such a space. Given their inherent complexity, our multi-faceted summaries benefit from a visualization environment. We discuss some essential features of such an environment.

Type
Papers
Copyright
© 2005 Cambridge University Press

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