Abstract
Automatically extracting key information from scientific documents has the potential to help scientists work more efficiently and accelerate the pace of scientific progress. Prior work has considered extracting document-level entity clusters and relations end-to-end from raw scientific text, which can improve literature search and help identify methods and materials for a given problem. Despite the importance of this task, most existing works on scientific information extraction (SciIE) consider extraction solely based on the content of an individual paper, without considering the paper’s place in the broader literature. In contrast to prior work, we augment our text representations by leveraging a complementary source of document context: the citation graph of referential links between citing and cited papers. On a test set of English-language scientific documents, we show that simple ways of utilizing the structure and content of the citation graph can each lead to significant gains in different scientific information extraction tasks. When these tasks are combined, we observe a sizable improvement in end-to-end information extraction over the state-of-the-art, suggesting the potential for future work along this direction. We release software tools to facilitate citation-aware SciIE development.- Anthology ID:
- 2021.acl-long.59
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 719–731
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.59
- DOI:
- 10.18653/v1/2021.acl-long.59
- Cite (ACL):
- Vijay Viswanathan, Graham Neubig, and Pengfei Liu. 2021. CitationIE: Leveraging the Citation Graph for Scientific Information Extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 719–731, Online. Association for Computational Linguistics.
- Cite (Informal):
- CitationIE: Leveraging the Citation Graph for Scientific Information Extraction (Viswanathan et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.acl-long.59.pdf
- Code
- viswavi/ScigraphIE
- Data
- S2ORC, SciREX