TY - GEN
T1 - Incorporating Peer Reviews and Rebuttal Counter-Arguments for Meta-Review Generation
AU - Wu, Po Cheng
AU - Yen, An Zi
AU - Huang, Hen Hsen
AU - Chen, Hsin Hsi
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/10/17
Y1 - 2022/10/17
N2 - Peer review is an essential part of the scientific process in which the research papers are assessed by several reviewers. The author rebuttal phase, which is held at most top conferences, provides an opportunity for the authors to defend their work against the arguments made by the reviewers. The strengths and the weaknesses pointed out by the reviewers, as well as the authors' responses, will be evaluated by the area chair. The final decisions generally accompany meta-reviews regarding the reason for acceptance/rejection. Previous research has studied the generation of meta-review using transformer-based summarization models. However, few of them consider the rebuttals' content and the interaction between reviews and rebuttals' arguments, where the argumentation persuasiveness plays an important role in affecting the final decision. To generate a comprehensive meta-review that well organizes reviewers' opinions and authors' responses, we present a novel generation model that is capable of explicitly modeling the complicated argumentation structure from not only arguments between the reviewers and the authors but also the inter-reviewer discussions. Experimental results show that our model outperforms baselines in terms of both automatic evaluation and human evaluation, demonstrating the effectiveness of our approach.
AB - Peer review is an essential part of the scientific process in which the research papers are assessed by several reviewers. The author rebuttal phase, which is held at most top conferences, provides an opportunity for the authors to defend their work against the arguments made by the reviewers. The strengths and the weaknesses pointed out by the reviewers, as well as the authors' responses, will be evaluated by the area chair. The final decisions generally accompany meta-reviews regarding the reason for acceptance/rejection. Previous research has studied the generation of meta-review using transformer-based summarization models. However, few of them consider the rebuttals' content and the interaction between reviews and rebuttals' arguments, where the argumentation persuasiveness plays an important role in affecting the final decision. To generate a comprehensive meta-review that well organizes reviewers' opinions and authors' responses, we present a novel generation model that is capable of explicitly modeling the complicated argumentation structure from not only arguments between the reviewers and the authors but also the inter-reviewer discussions. Experimental results show that our model outperforms baselines in terms of both automatic evaluation and human evaluation, demonstrating the effectiveness of our approach.
KW - argument mining
KW - counter-argument identification
KW - meta-review generation
UR - http://www.scopus.com/inward/record.url?scp=85140830746&partnerID=8YFLogxK
U2 - 10.1145/3511808.3557360
DO - 10.1145/3511808.3557360
M3 - Conference contribution
AN - SCOPUS:85140830746
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2189
EP - 2198
BT - CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 31st ACM International Conference on Information and Knowledge Management, CIKM 2022
Y2 - 17 October 2022 through 21 October 2022
ER -