Abstract
Highlighting while reading is a natural behavior for people to track salient content of a document. It would be desirable to teach an extractive summarizer to do the same. However, a major obstacle to the development of a supervised summarizer is the lack of ground-truth. Manual annotation of extraction units is cost-prohibitive, whereas acquiring labels by automatically aligning human abstracts and source documents can yield inferior results. In this paper we describe a novel framework to guide a supervised, extractive summarization system with question-answering rewards. We argue that quality summaries should serve as document surrogates to answer important questions, and such question-answer pairs can be conveniently obtained from human abstracts. The system learns to promote summaries that are informative, fluent, and perform competitively on question-answering. Our results compare favorably with those reported by strong summarization baselines as evaluated by automatic metrics and human assessors.- Anthology ID:
- N19-1264
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2566–2577
- Language:
- URL:
- https://aclanthology.org/N19-1264
- DOI:
- 10.18653/v1/N19-1264
- Cite (ACL):
- Kristjan Arumae and Fei Liu. 2019. Guiding Extractive Summarization with Question-Answering Rewards. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2566–2577, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Guiding Extractive Summarization with Question-Answering Rewards (Arumae & Liu, NAACL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/N19-1264.pdf
- Code
- ucfnlp/summ_qa_rewards
- Data
- CNN/Daily Mail