2021/09/28 by Ana Sabina Uban, Uban, Ana Sabina, Cornelia Caragea +1
Computer Science · Engineering · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computer science #Data science #Engineering #FOS: Computer and information sciences #Machine Learning (cs.LG) #Management science #Natural Language Processing Techniques #Topic Modeling #cs.AI #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.2109.14059
arxiv created 2021/09/28 · openalex publication_date 2021/09/28 · arxiv updated 2021/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The review process is essential to ensure the quality of publications. Recently, the increase of submissions for top venues in machine learning and NLP has caused a problem of excessive burden on reviewers and has often caused concerns regarding how this may not only overload reviewers, but also may affect the quality of the reviews. An automatic system for assisting with the reviewing process could be a solution for ameliorating the problem. In this paper, we explore automatic review summary generation for scientific papers. We posit that neural language models have the potential to be valuable candidates for this task. In order to test this hypothesis, we release a new dataset of scientific papers and their reviews, collected from papers published in the NeurIPS conference from 2013 to 2020. We evaluate state of the art neural summarization models, present initial results on the feasibility of automatic review summary generation, and propose directions for the future.