Summary of Seagraph: Unveiling the Whole Story Of Paper Review Comments, by Jianxiang Yu et al.
SEAGraph: Unveiling the Whole Story of Paper Review Comments
by Jianxiang Yu, Jiaqi Tan, Zichen Ding, Jiapeng Zhu, Jiahao Li, Yao Cheng, Qier Cui, Yunshi Lan, Xiang Li
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework called SEAGraph is introduced to clarify peer review comments by uncovering the underlying intentions behind them. The framework constructs two types of graphs for each paper: a semantic mind graph capturing the author’s thought process and a hierarchical background graph delineating research domains related to the paper. A retrieval method extracts relevant content from both graphs, facilitating coherent explanations for review comments. Extensive experiments demonstrate SEAGraph’s effectiveness in review comment understanding tasks, offering significant benefits to authors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Peer review is crucial for ensuring the quality of scientific research. However, traditional peer reviews often provide vague or insufficient feedback, making it hard for authors to improve their work. To address this issue, a new framework called SEAGraph has been developed. It helps authors understand reviewer comments by analyzing the underlying intentions behind them. The framework uses two types of graphs: one that shows how an author thinks and another that explains related research domains. This makes it easier for authors to see what they need to improve and how to do it. |
