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Academic Paper


Title: Visualization-enabled multi-document summarization by Iterative Residual Rescaling
Author: Branimir Boguraev
Institution: IBM T.J. Watson Research Center
Author: Roy Byrd
Institution: IBM T.J. Watson Research Center
Author: Mary Neff
Institution: IBM T.J. Watson Research Center
Linguistic Field: Computational Linguistics
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.

CUP AT LINGUIST

This article appears IN Natural Language Engineering Vol. 11, Issue 1, which you can READ on Cambridge's site or on LINGUIST .



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