ReviewRobot：基于知识综合的可解释论文评审生成
ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis
- University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
- DiDi Labs（滴滴实验室）
- Salesforce Research（赛富时研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出 ReviewRobot，通过构建并比较目标论文、相关工作与背景知识图谱生成评分、证据和模板化评论，实现可解释的论文评审辅助。
AI中文摘要：
为辅助人工评审流程，我们构建了一种新颖的 ReviewRobot，用于自动给出评审分数，并为创新性、有意义的比较等多个类别撰写评论。好的评审需要具备知识性，即评论应当具有建设性和信息量，以帮助改进论文；同时还应具备可解释性，能够提供详细证据。ReviewRobot 通过三个步骤实现这些目标：（1）我们执行面向特定领域的信息抽取，从正在评审的目标论文构建知识图谱（KG），从目标论文引用的论文构建相关工作 KG，并从该领域大量以往论文集合中构建背景 KG。（2）通过比较这三个 KG，我们为每个评审类别预测评审分数以及作为证据的详细结构化知识。（3）我们仔细筛选人工评审语句并将其泛化为模板，并应用这些模板把评审分数和证据转换为自然语言评论。实验结果表明，我们的评审分数预测器达到 71.4%–100% 的准确率。领域专家的人工评估显示，ReviewRobot 生成的评论中有 41.7%–70.5% 是有效且具有建设性的，并且在 20% 的情况下优于人工撰写的评论。因此，ReviewRobot 可以作为论文审稿人、程序委员会主席和作者的助手。
英文摘要：
To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison. A good review needs to be knowledgeable, namely that the comments should be constructive and informative to help improve the paper; and explainable by providing detailed evidence. ReviewRobot achieves these goals via three steps: (1) We perform domain-specific Information Extraction to construct a knowledge graph (KG) from the target paper under review, a related work KG from the papers cited by the target paper, and a background KG from a large collection of previous papers in the domain. (2) By comparing these three KGs, we predict a review score and detailed structured knowledge as evidence for each review category. (3) We carefully select and generalize human review sentences into templates, and apply these templates to transform the review scores and evidence into natural language comments. Experimental results show that our review score predictor reaches 71.4%-100% accuracy. Human assessment by domain experts shows that 41.7%-70.5% of the comments generated by ReviewRobot are valid and constructive, and better than human-written ones for 20% of the time. Thus, ReviewRobot can serve as an assistant for paper reviewers, program chairs and authors.
