Multi-candidate reduction: Sentence compression as a tool for document summarization tasks

Authors:
David Zajic;Bonnie J. Dorr;Jimmy Lin;Richard Schwartz
Affiliations:
University of Maryland, College Park, MD 20742, United States;University of Maryland, College Park, MD 20742, United States;University of Maryland, College Park, MD 20742, United States;BBN Technologies, 9861 Broken Land Parkway, Columbia, MD 21046, United States
Venue:
Information Processing and Management: an International Journal
Year:
2007

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Abstract

This article examines the application of two single-document sentence compression techniques to the problem of multi-document summarization-a ''parse-and-trim'' approach and a statistical noisy-channel approach. We introduce the multi-candidate reduction (MCR) framework for multi-document summarization, in which many compressed candidates are generated for each source sentence. These candidates are then selected for inclusion in the final summary based on a combination of static and dynamic features. Evaluations demonstrate that sentence compression is a valuable component of a larger multi-document summarization framework.