With the ever-increasing number of submissions in top-tier conferences and journals, finding good reviewers and meta-reviewers is becoming increasingly difficult. Writing a meta-review is not straightforward as it involves a series of sub-tasks, including making a decision on the paper based on the reviewer’s recommendation and their confidence in the recommendation, mitigating disagreements among the reviewers, and other such similar tasks. In this work, we develop a novel approach to automatically generate meta-reviews that are decision-aware and which also take into account a set of relevant sub-tasks in the peer-review process. More specifically, we first predict the recommendation scores and confidence scores for the reviews, using which we then predict the decision on a particular manuscript. Finally, we utilize the decision signals for generating the meta-reviews using a transformer-based seq2seq architecture. Our proposed pipelined approach for automatic decision-aware meta-review generation achieves significant performance improvement over the standard summarization baselines as well as relevant prior works on this problem. We make our codes available at https://github.com/saprativa/seq-to-seq-decision-aware-mrg.

随着顶级会议和期刊的投稿数量不断增加，寻找优秀的审稿人和元审稿人变得越来越困难。撰写元评论并不简单，因为它涉及一系列子任务，包括根据审稿人的建议和他们对建议的信心对论文做出决定，减少审稿人之间的分歧，以及其他类似的任务。在这项工作中，我们开发了一种新方法来自动生成具有决策意识的元评论，并且还考虑了同行评审过程中的一组相关子任务。更具体地说，我们首先预测评论的推荐分数和置信度分数，然后我们使用它们来预测对特定手稿的决定。最后，我们利用决策信号使用基于 transformer 的 seq2seq 架构生成元评论。我们提出的用于自动决策感知元审查生成的流水线方法与标准摘要基线以及有关此问题的相关先前工作相比实现了显着的性能改进。我们在 https://github.com/saprativa/seq-to-seq-decision-aware-mrg 上提供我们的代码。