Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead

Khalid Al Khatib, Tirthankar Ghosal, Yufang Hou, Anita de Waard, Dayne Freitag


Abstract
Argument mining targets structures in natural language related to interpretation and persuasion which are central to scientific communication. Most scholarly discourse involves interpreting experimental evidence and attempting to persuade other scientists to adopt the same conclusions. While various argument mining studies have addressed student essays and news articles, those that target scientific discourse are still scarce. This paper surveys existing work in argument mining of scholarly discourse, and provides an overview of current models, data, tasks, and applications. We identify a number of key challenges confronting argument mining in the scientific domain, and suggest some possible solutions and future directions.
Anthology ID:
2021.sdp-1.7
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:
56–65
Language:
URL:
https://aclanthology.org/2021.sdp-1.7
DOI:
10.18653/v1/2021.sdp-1.7
Bibkey:
Cite (ACL):
Khalid Al Khatib, Tirthankar Ghosal, Yufang Hou, Anita de Waard, and Dayne Freitag. 2021. Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead. In Proceedings of the Second Workshop on Scholarly Document Processing, pages 56–65, Online. Association for Computational Linguistics.
Cite (Informal):
Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead (Al Khatib et al., sdp 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.sdp-1.7.pdf