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

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.

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