ReviseMate: Exploring Contextual Support for Digesting STEM Paper Reviews

August 21, 2025 Β· Declared Dead Β· πŸ› Proc. ACM Hum. Comput. Interact.

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Authors Yuansong Xu, Shuhao Zhang, Yijie Fan, Shaohan Shi, Zhenhui Peng, Quan Li arXiv ID 2508.15148 Category cs.HC: Human-Computer Interaction Citations 1 Venue Proc. ACM Hum. Comput. Interact. Last Checked 4 months ago
Abstract
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 phase. Through interviews and the creation of storyboards, we developed ReviseMate, an interactive system designed to address the identified challenges. A controlled user study (N=31) demonstrated the superiority of ReviseMate over baseline methods, with positive feedback regarding user interaction. Subsequent field deployment (N=6) further validated the effectiveness of ReviseMate in real-world review digestion scenarios. These findings underscore the potential of interactive tools to significantly enhance the assimilation and integration of reviewer feedback during the manuscript review process.
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