A Redundancy-Aware Sentence Regression Framework for Extractive Summarization
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.- Anthology ID:
- C16-1004
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 33–43
- Language:
- URL:
- https://aclanthology.org/C16-1004
- DOI:
- Cite (ACL):
- Pengjie Ren, Furu Wei, Zhumin Chen, Jun Ma, and Ming Zhou. 2016. A Redundancy-Aware Sentence Regression Framework for Extractive Summarization. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 33–43, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- A Redundancy-Aware Sentence Regression Framework for Extractive Summarization (Ren et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/C16-1004.pdf