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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis
%A Wang, Qingyun
%A Zeng, Qi
%A Huang, Lifu
%A Knight, Kevin
%A Ji, Heng
%A Rajani, Nazneen Fatema
%Y Davis, Brian
%Y Graham, Yvette
%Y Kelleher, John
%Y Sripada, Yaji
%S Proceedings of the 13th International Conference on Natural Language Generation
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland
%F wang-etal-2020-reviewrobot
%X 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.
%R 10.18653/v1/2020.inlg-1.44
%U https://aclanthology.org/2020.inlg-1.44/
%U https://doi.org/10.18653/v1/2020.inlg-1.44
%P 384-397

Markdown (Informal)

[ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis](https://aclanthology.org/2020.inlg-1.44/) (Wang et al., INLG 2020)

• ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis (Wang et al., INLG 2020)

ACL

• Qingyun Wang, Qi Zeng, Lifu Huang, Kevin Knight, Heng Ji, and Nazneen Fatema Rajani. 2020. ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis. In Proceedings of the 13th International Conference on Natural Language Generation, pages 384–397, Dublin, Ireland. Association for Computational Linguistics.