@inproceedings{al-khatib-etal-2021-argument,
title = "Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead",
author = "Al Khatib, Khalid and
Ghosal, Tirthankar and
Hou, Yufang and
de Waard, Anita and
Freitag, Dayne",
editor = "Beltagy, Iz and
Cohan, Arman and
Feigenblat, Guy and
Freitag, Dayne and
Ghosal, Tirthankar and
Hall, Keith and
Herrmannova, Drahomira and
Knoth, Petr and
Lo, Kyle and
Mayr, Philipp and
Patton, Robert M. and
Shmueli-Scheuer, Michal and
de Waard, Anita and
Wang, Kuansan and
Wang, Lucy Lu",
booktitle = "Proceedings of the Second Workshop on Scholarly Document Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.sdp-1.7",
doi = "10.18653/v1/2021.sdp-1.7",
pages = "56--65",
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.",
}
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%0 Conference Proceedings
%T Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead
%A Al Khatib, Khalid
%A Ghosal, Tirthankar
%A Hou, Yufang
%A de Waard, Anita
%A Freitag, Dayne
%Y Beltagy, Iz
%Y Cohan, Arman
%Y Feigenblat, Guy
%Y Freitag, Dayne
%Y Ghosal, Tirthankar
%Y Hall, Keith
%Y Herrmannova, Drahomira
%Y Knoth, Petr
%Y Lo, Kyle
%Y Mayr, Philipp
%Y Patton, Robert M.
%Y Shmueli-Scheuer, Michal
%Y de Waard, Anita
%Y Wang, Kuansan
%Y Wang, Lucy Lu
%S Proceedings of the Second Workshop on Scholarly Document Processing
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F al-khatib-etal-2021-argument
%X 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.
%R 10.18653/v1/2021.sdp-1.7
%U https://aclanthology.org/2021.sdp-1.7
%U https://doi.org/10.18653/v1/2021.sdp-1.7
%P 56-65
Markdown (Informal)
[Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead](https://aclanthology.org/2021.sdp-1.7) (Al Khatib et al., sdp 2021)
ACL