@InProceedings{ren-EtAl:2016:COLING,
  author    = {Ren, Pengjie  and  Wei, Furu  and  CHEN, Zhumin  and  MA, Jun  and  Zhou, Ming},
  title     = {A Redundancy-Aware Sentence Regression Framework for Extractive Summarization},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {33--43},
  abstract  = {Existing sentence regression methods for extractive summarization usually model
	sentence importance and redundancy in two separate processes. They first
	evaluate the importance f(s) of each sentence s and then select sentences to
	generate a summary based on both the importance scores and redundancy among
	sentences. In this paper, we propose to model importance and redundancy
	simultaneously by directly evaluating the relative importance f(s|S) of a
	sentence s given a set of selected sentences S. Specifically, we present a new
	framework to conduct regression with respect to the relative gain of s given S
	calculated by the ROUGE metric. Besides the single sentence features,
	additional features derived from the sentence relations are incorporated.
	Experiments on the DUC 2001, 2002 and 2004 multi-document summarization
	datasets show that the proposed method outperforms state-of-the-art extractive
	summarization approaches.},
  url       = {http://aclweb.org/anthology/C16-1004}
}