Arxiv.org  April 8, 2021 The number of scientific papers generated has skyrocketed. Providing high-quality reviews of this growing number of papers is a significant challenge. Researchers at Carnegie Mellon University discuss the possibility of using state-of-the-art natural language processing (NLP) models to generate first-pass peer reviews for scientific papers. They collected a dataset of papers in the machine learning domain, annotated them with different aspects of content covered in each review, and trained targeted summarization models that take in papers to generate reviews. The results showed that system-generated reviews tend to touch upon more aspects of the paper than human-written […]