%0 Journal Article %J Information Processing & Management %D 2007 %T Multi-candidate reduction: Sentence compression as a tool for document summarization tasks %A Zajic, David %A Dorr, Bonnie J %A Jimmy Lin %A Schwartz,Richard %K Headline generation %K Hidden Markov model %K Parse-and-trim %K Summarization %X 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. %B Information Processing & Management %V 43 %P 1549 - 1570 %8 2007/11// %@ 0306-4573 %G eng %U http://www.sciencedirect.com/science/article/pii/S0306457307000295 %N 6 %R 10.1016/j.ipm.2007.01.016