Abstract
In this paper, we study the abstractive sentence summarization. There are two essential information features that can influence the quality of news summarization, which are topic keywords and the knowledge structure of the news text. Besides, the existing knowledge encoder has poor performance on sparse sentence knowledge structure. Considering these, we propose KAS, a novel Knowledge and Keywords Augmented Abstractive Sentence Summarization framework. Tri-encoders are utilized to integrate contexts of original text, knowledge structure and keywords topic simultaneously, with a special linearized knowledge structure. Automatic and human evaluations demonstrate that KAS achieves the best performances.- Anthology ID:
- 2021.newsum-1.3
- Volume:
- Proceedings of the Third Workshop on New Frontiers in Summarization
- Month:
- November
- Year:
- 2021
- Address:
- Online and in Dominican Republic
- Venue:
- newsum
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 25–32
- Language:
- URL:
- https://aclanthology.org/2021.newsum-1.3
- DOI:
- 10.18653/v1/2021.newsum-1.3
- Cite (ACL):
- Shuo Guan. 2021. Knowledge and Keywords Augmented Abstractive Sentence Summarization. In Proceedings of the Third Workshop on New Frontiers in Summarization, pages 25–32, Online and in Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Knowledge and Keywords Augmented Abstractive Sentence Summarization (Guan, newsum 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.newsum-1.3.pdf