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/proper-vol2-ingestion/C16-1004.pdf