引入同儕審查及答辯中反論點之綜合審稿評論生成

同儕審查(Peer review)為學術領域中相當重要的一環，在審查的過程中，研究論文會交由數位審稿人進行評估。大多數的頂尖會議上皆會舉行作者答辯的階段，提供投稿作者答覆審稿人評論論點的機會，以穩固他們的投稿作品。審稿人對於投稿文章所指出的優缺點，以及投稿作者相對應的答覆，將一併交由領域主席(area chair)做最後的評估，而在做此最終決定的同時也會撰寫綜合審稿評論(meta-review)說明予以錄用或拒絕的原因。過往的研究中有嘗試透過基於Transformer結構的摘要生成模型以做綜合審稿評論生成，然而，較少研究有考慮到作者答覆的內容以及評論及答辯中論點的交互關係，其答辯論證中的說服力在做最終決定時有著重要的影響力。為了生成能夠良好彙整審稿人論點與作者答覆的綜合審稿評論，我們提出了一個新的生成模型得以明確地引入審稿時複雜的論證結構，以了解審稿人與作者間、以及跨評論間論點的交互關係。實驗結果顯示我們的模型在自動化評估及人工評斷下相較於其他現行模型皆取得更好的表現，說明我們所提出的方法的有效性。

關鍵字

綜合審稿評論生成；論點探勘；反論點辨識

並列摘要

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.

並列關鍵字

Meta-Review Generation ； Argument Mining ； Counter-Argument Identification

參考文獻

Ke Wang and Xiaojun Wan. Sentiment analysis of peer review texts for scholarly papers. In The 41st International ACM SIGIR Conference on Research Development

Google Scholar

in Information Retrieval, pages 175–184, 2018.

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Tirthankar Ghosal, Rajeev Verma, Asif Ekbal, and Pushpak Bhattacharyya. Deepsentipeer: Harnessing sentiment in review texts to recommend peer review decisions. In Proceedings of the 57th Annual Meeting of the Association for

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Computational Linguistics, pages 1120–1130, 2019.

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Shruti Singh, Mayank Singh, and Pawan Goyal. Compare: a taxonomy and dataset of comparison discussions in peer reviews. arXiv preprint arXiv:2108.04366, 2021.

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引入同儕審查及答辯中反論點之綜合審稿評論生成

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