Abstract
This paper describes our submission for the LongSumm task in SDP 2021. We propose a method for incorporating sentence embeddings produced by deep language models into extractive summarization techniques based on graph centrality in an unsupervised manner. The proposed method is simple, fast, can summarize any kind of document of any size and can satisfy any length constraints for the summaries produced. The method offers competitive performance to more sophisticated supervised methods and can serve as a proxy for abstractive summarization techniques- Anthology ID:
- 2021.sdp-1.14
- 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:
- 110–115
- Language:
- URL:
- https://aclanthology.org/2021.sdp-1.14
- DOI:
- 10.18653/v1/2021.sdp-1.14
- Cite (ACL):
- Juan Ramirez-Orta and Evangelos Milios. 2021. Unsupervised document summarization using pre-trained sentence embeddings and graph centrality. In Proceedings of the Second Workshop on Scholarly Document Processing, pages 110–115, Online. Association for Computational Linguistics.
- Cite (Informal):
- Unsupervised document summarization using pre-trained sentence embeddings and graph centrality (Ramirez-Orta & Milios, sdp 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2021.sdp-1.14.pdf