SEAGraph: Unveiling the Whole Story of Paper Review Comments

2024-12-16

arXiv.org

AI Summary

To address the problem of ambiguous reviewer comments in peer review—leading to difficulties in author comprehension, prolonged revision cycles, and limited manuscript improvement—this paper proposes a dual-graph collaborative modeling framework. It constructs a semantic mental graph (explicitly modeling the author’s cognitive pathway) and a hierarchical background graph (structurally embedding domain-specific knowledge), jointly optimized via graph neural network–driven retrieval for intent-level feedback interpretation. This work is the first to unify and co-reason over the author’s thought process and domain knowledge structure. Experiments demonstrate significant improvements: +23.6% in key-point identification accuracy and +31.4% in explanation consistency over prior state-of-the-art methods across multiple benchmark tasks. Empirical evaluation further shows an average 37% reduction in revision cycle time, enhancing both transparency and actionability of peer review feedback.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Reasoning under Uncertainty: Graphical Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Addresses vague peer review feedback hindering manuscript improvement
• Enhances author comprehension of reviewers' underlying critique intentions
• Bridges communication gap between reviewers and authors in publishing

Innovation

• SEAGraph framework clarifies review comments via intention analysis
• Constructs semantic mind graphs and hierarchical background graphs
• Uses retrieval method to extract relevant content for explanations