ReviseMate: Exploring Contextual Support for Digesting STEM Paper Reviews

Fig. 1. An example scenario illustrating how authors can effectively digest reviews of their papers using ReviseMate: (A) The author encounters challenges when attempting to comprehend the feedback provided by reviewers for his paper. (B) In search of assistance, the author turns to ReviseMate. By leveraging the system's capabilities in the analysis process, including comment extraction, aligning comments with relevant paragraphs in the original paper, comment categorization, and the integration of analyzed information, the author successfully engages in a contextual and tailored review text digestion process. This figure was created with the collaboration of an AI-powered image generation tool Midjourney.

Effectively assimilating and integrating reviewer feedback is crucial for researchers seeking to refine their papers and handle potential rebuttal phases in academic venues. However, traditional review digestion processes present challenges such as time consumption, reading fatigue, and the requisite for comprehensive analytical skills. Prior research on review analysis often provides theoretical guidance with limited targeted support. Additionally, general text comprehension tools overlook the intricate nature of comprehensively understanding reviews and lack contextual assistance. To bridge this gap, we formulated research questions to explore the authors' concerns and methods for enhancing comprehension during the review digestion * This work was done when Yijie Fan was an undergraduate student at ShanghaiTech University.