@inproceedings{ren-etal-2016-redundancy,
title = "A Redundancy-Aware Sentence Regression Framework for Extractive Summarization",
author = "Ren, Pengjie and
Wei, Furu and
Chen, Zhumin and
Ma, Jun and
Zhou, Ming",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/C16-1004/",
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."
}
Markdown (Informal)
[A Redundancy-Aware Sentence Regression Framework for Extractive Summarization](https://preview.aclanthology.org/jlcl-multiple-ingestion/C16-1004/) (Ren et al., COLING 2016)
ACL