MetaGen: An academic Meta-review Generation system

Abstract

Peer reviews form an essential part of scientific communications. Research papers and proposals are reviewed by several peers before they are finally accepted or rejected. The procedure followed requires experts to review the research work. Then the area/program chair/editor writes a meta-review summarizing the review comments and taking a call based on the reviewers' decisions. In this paper, we present MetaGen, a novel meta-review generation system which takes the peer reviews as input and produces an assistive meta-review. This meta-review generation can help the area/program chair writing a meta-review and taking the final decision on the paper/proposal. Thus it can also help to speed up the review process for conference/journals where a large number of submissions need to be handled within a stipulated time. Our approach first generates an extractive draft and then uses fine-tuned UniLM (Unified Langauge Model) for predicting the acceptance decision and making the final meta-review in an abstractive manner. To the best of our knowledge, this is the first work in the direction of meta-review generation. Evaluation based on ROUGE score shows promising results and comparison with few state-of-the-art summarizers demonstrates the effectiveness of the system. © 2020 ACM.