Abstract
Most summarization task focuses on generating relatively short summaries. Such a length constraint might not be appropriate when summarizing scientific work. The LongSumm task needs participants generate long summary for scientific document. This task usual can be solved by language model. But an important problem is that model like BERT is limit to memory, and can not deal with a long input like a document. Also generate a long output is hard. In this paper, we propose a session based automatic summarization model(SBAS) which using a session and ensemble mechanism to generate long summary. And our model achieves the best performance in the LongSumm task.- Anthology ID:
- 2021.sdp-1.12
- Volume:
- Proceedings of the Second Workshop on Scholarly Document Processing
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Iz Beltagy, Arman Cohan, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Keith Hall, Drahomira Herrmannova, Petr Knoth, Kyle Lo, Philipp Mayr, Robert M. Patton, Michal Shmueli-Scheuer, Anita de Waard, Kuansan Wang, Lucy Lu Wang
- Venue:
- sdp
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 97–102
- Language:
- URL:
- https://aclanthology.org/2021.sdp-1.12
- DOI:
- 10.18653/v1/2021.sdp-1.12
- Cite (ACL):
- Senci Ying, Zheng Yan Zhao, and Wuhe Zou. 2021. LongSumm 2021: Session based automatic summarization model for scientific document. In Proceedings of the Second Workshop on Scholarly Document Processing, pages 97–102, Online. Association for Computational Linguistics.
- Cite (Informal):
- LongSumm 2021: Session based automatic summarization model for scientific document (Ying et al., sdp 2021)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2021.sdp-1.12.pdf