Abstract
We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of document-summary pairs. When used for single-document summarization, our approach, combined with the “beginning of document” heuristic, outperforms a state-of-the-art summarizer and the beginning-of-article baseline in both automatic and manual evaluations. These results represent an important advance because in the absence of cross-document repetition, single document summarizers for news have not been able to consistently outperform the strong beginning-of-article baseline.- Anthology ID:
- E17-2112
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 707–712
- Language:
- URL:
- https://aclanthology.org/E17-2112
- DOI:
- Cite (ACL):
- Yinfei Yang, Forrest Bao, and Ani Nenkova. 2017. Detecting (Un)Important Content for Single-Document News Summarization. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 707–712, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Detecting (Un)Important Content for Single-Document News Summarization (Yang et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/E17-2112.pdf