Abstract
The success of neural summarization models stems from the meticulous encodings of source articles. To overcome the impediments of limited and sometimes noisy training data, one promising direction is to make better use of the available training data by applying filters during summarization. In this paper, we propose a novel Bi-directional Selective Encoding with Template (BiSET) model, which leverages template discovered from training data to softly select key information from each source article to guide its summarization process. Extensive experiments on a standard summarization dataset are conducted and the results show that the template-equipped BiSET model manages to improve the summarization performance significantly with a new state of the art.- Anthology ID:
- P19-1207
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2153–2162
- Language:
- URL:
- https://aclanthology.org/P19-1207
- DOI:
- 10.18653/v1/P19-1207
- Cite (ACL):
- Kai Wang, Xiaojun Quan, and Rui Wang. 2019. BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2153–2162, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization (Wang et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P19-1207.pdf
- Code
- InitialBug/BiSET