Towards automated meta-review generation via an NLP/ML pipeline in different stages of the scholarly peer review process

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.