Abstract
Local citation recommendation aims at finding articles relevant for given citation context. While most previous approaches represent context using solely text surrounding the citation, we propose enhancing context representation with global information. Specifically, we include citing article’s title and abstract into context representation. We evaluate our model on datasets with different citation context sizes and demonstrate improvements with globally-enhanced context representations when citation contexts are smaller.- Anthology ID:
- 2020.sdp-1.11
- Volume:
- Proceedings of the First Workshop on Scholarly Document Processing
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Muthu Kumar Chandrasekaran, Anita de Waard, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Eduard Hovy, Petr Knoth, David Konopnicki, Philipp Mayr, Robert M. Patton, Michal Shmueli-Scheuer
- Venue:
- sdp
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 97–103
- Language:
- URL:
- https://aclanthology.org/2020.sdp-1.11
- DOI:
- 10.18653/v1/2020.sdp-1.11
- Cite (ACL):
- Zoran Medić and Jan Snajder. 2020. Improved Local Citation Recommendation Based on Context Enhanced with Global Information. In Proceedings of the First Workshop on Scholarly Document Processing, pages 97–103, Online. Association for Computational Linguistics.
- Cite (Informal):
- Improved Local Citation Recommendation Based on Context Enhanced with Global Information (Medić & Snajder, sdp 2020)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2020.sdp-1.11.pdf
- Code
- zoranmedic/duallcr
- Data
- RefSeer