Overview of the DAGPap22 Shared Task on Detecting Automatically Generated Scientific Papers

Yury Kashnitsky, Drahomira Herrmannova, Anita de Waard, George Tsatsaronis, Catriona Catriona Fennell, Cyril Labbe

Abstract
This paper provides an overview of the DAGPap22 shared task on the detection of automatically generated scientific papers at the Scholarly Document Process workshop colocated with COLING. We frame the detection problem as a binary classification task: given an excerpt of text, label it as either human-written or machine-generated. We shared a dataset containing excerpts from human-written papers as well as artificially generated content and suspicious documents collected by Elsevier publishing and editorial teams. As a test set, the participants are provided with a 5x larger corpus of openly accessible human-written as well as generated papers from the same scientific domains of documents. The shared task saw 180 submissions across 14 participating teams and resulted in two published technical reports. We discuss our findings from the shared task in this overview paper.
Anthology ID:
2022.sdp-1.26
Volume:
Proceedings of the Third Workshop on Scholarly Document Processing
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Arman Cohan, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Drahomira Herrmannova, Petr Knoth, Kyle Lo, Philipp Mayr, Michal Shmueli-Scheuer, Anita de Waard, Lucy Lu Wang
Venue:
sdp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
210–213
Language:
URL:
https://aclanthology.org/2022.sdp-1.26
DOI:
Bibkey:
Cite (ACL):
Yury Kashnitsky, Drahomira Herrmannova, Anita de Waard, George Tsatsaronis, Catriona Catriona Fennell, and Cyril Labbe. 2022. Overview of the DAGPap22 Shared Task on Detecting Automatically Generated Scientific Papers. In Proceedings of the Third Workshop on Scholarly Document Processing, pages 210–213, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
Overview of the DAGPap22 Shared Task on Detecting Automatically Generated Scientific Papers (Kashnitsky et al., sdp 2022)
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