Peer review, as a cornerstone of scientific research, ensures the integrity and quality of scholarly work by providing authors with objective feedback for refinement. However, in the traditional peer review process, authors often receive vague or insufficiently detailed feedback, which provides limited assistance and leads to a more time-consuming review cycle. If authors can identify some specific weaknesses in their paper, they can not only address the reviewer's concerns but also improve their work. This raises the critical question of how to enhance authors' comprehension of review comments. In this paper, we present SEAGraph, a novel framework developed to clarify review comments by uncovering the underlying intentions behind them. We construct two types of graphs for each paper: the semantic mind graph, which captures the authors' thought process, and the hierarchical background graph, which delineates the research domains related to the paper. A retrieval method is then designed to extract relevant content from both graphs, facilitating coherent explanations for the review comments. Extensive experiments show that SEAGraph excels in review comment understanding tasks, offering significant benefits to authors. By bridging the gap between reviewers' critiques and authors' comprehension, SEAGraph contributes to a more efficient, transparent and collaborative scientific publishing ecosystem.

翻译：同行评审作为科学研究的基石，通过向作者提供客观反馈以完善其工作，确保了学术研究的完整性与质量。然而，在传统的同行评审过程中，作者往往收到模糊或不够详细的反馈，这不仅提供的帮助有限，还导致评审周期更为耗时。如果作者能够识别其论文中的某些具体不足，他们不仅能回应评审人的关切，还能提升自身工作质量。这引出了一个关键问题：如何增强作者对评审意见的理解？本文提出SEAGraph，一种旨在通过揭示评审意见背后潜在意图来澄清意见的新颖框架。我们为每篇论文构建两种图：语义思维图（捕捉作者的思考过程）和层次背景图（描绘与论文相关的研究领域）。随后设计了一种检索方法，从两种图中提取相关内容，从而为评审意见提供连贯的解释。大量实验表明，SEAGraph在评审意见理解任务中表现优异，为作者带来了显著益处。通过弥合评审人批评与作者理解之间的差距，SEAGraph有助于构建更高效、透明和协作的科学出版生态系统。