DeepSentiPeer: Harnessing Sentiment in Review Texts to Recommend Peer Review Decisions (P19-1)
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| Challenge: | Existing peer review system is not straightforward and requires domain knowledge, expertise, and intelligence of human reviewers, which is somewhat elusive with the current state of AI. |
| Approach: | They propose to use peer review texts to predict acceptance or rejection of a manuscript based on reviewer sentiment. |
| Outcome: | The proposed deep neural architecture achieves significant performance improvement over baselines (29% error reduction) in a recently released dataset of peer reviews. |
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| Challenge: | Existing systems that match papers with experts are inefficient and often require long turnaround times. |
| Approach: | They propose an autonomous peer reviewer selection system that employs the natural language processing model to match submitted papers with expert reviewers independently of traditional journals and conferences. |
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Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future (2026.acl-long)
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Sihong Wu, Owen Jiang, Yilun Zhao, Tiansheng Hu, Yiling Ma, Kaiyan Zhang, Manasi Patwardhan, Arman Cohan
| Challenge: | Recent advances in large language models (LLMs) motivated methods that assist or automate different stages of peer review pipeline. |
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Generative Reviewer Agents: Scalable Simulacra of Peer Review (2025.emnlp-industry)
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| Challenge: | Existing peer review mechanisms are limited by the small fraction of researchers with established networks. |
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ReviewEval: An Evaluation Framework for AI-Generated Reviews (2025.findings-emnlp)
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| Challenge: | escalating volume of academic research necessitates innovative approaches to peer review . authors propose reviewEval, ReviewAgent and ReviewEval to improve on existing reviews . |
| Approach: | They propose a framework for AI-generated reviews that measures alignment with human assessments . they propose 'reviewAgent' that iteratively optimizes its intermediate outputs and external improvement loops . |
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DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process (2025.acl-long)
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| Challenge: | Existing Large Language Models (LLMs) face limited domain expertise, hallucinated reasoning, and a lack of structured evaluation. |
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Argument Mining for Understanding Peer Reviews (N19-1)
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| Challenge: | In 2015 alone, approximately 63.4 million hours were spent on peer reviews. |
| Approach: | They propose to automatically detect argumentative propositions put forward by reviewers and their types by automatically detecting their types and types. |
| Outcome: | The proposed method detects (1) the argumentative propositions put forward by reviewers, and (2) their types (e.g., evaluating the work or making suggestions for improvement). |
A Neural Citation Count Prediction Model based on Peer Review Text (D19-1)
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| Challenge: | Recent studies have focused on extracting or mining useful features from the paper itself or the associated authors. |
| Approach: | They propose to utilize peer review data for the CCP task with a neural prediction model to learn a comprehensive semantic representation for peer review text. |
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Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation (2020.coling-main)
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| Challenge: | Existing methods for review rating prediction ignore hierarchies among data . paper review rating predictions are important for improving paper review process . |
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Towards Opinion Summarization of Customer Reviews (P18-3)
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| Challenge: | Existing methods to summarize text are limited to small, homogeneous datasets . authors outline future directions to solve these problems . |
| Approach: | They propose to use neural networks to generate summaries of user-generated travel reviews . they aim to take into account shifting opinions over time and address these issues . |
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A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications (N18-1)
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Dongyeop Kang, Waleed Ammar, Bhavana Dalvi, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz
| Challenge: | a dataset of 14.7K paper drafts and accept/reject decisions in top-tier venues including ACL, NIPS and ICLR is presented to study peer reviews. |
| Approach: | They propose to use the dataset to collect peer reviews from top-tier venues including ACL, NIPS and ICLR and to use it to create a dataset of peer reviews for research purposes. |
| Outcome: | The proposed dataset includes 14.7K paper drafts and accept/reject decisions in top-tier venues including ACL, NIPS and ICLR. |