The paper discusses the potential of Natural Language Processing (NLP) to enhance the peer review process, which is crucial for ensuring the quality and integrity of scientific publications. As the volume of scientific articles increases, the traditional peer review process becomes increasingly challenging due to its complexity, time consumption, and susceptibility to errors. NLP has the potential to address these issues by automating various aspects of the peer review workflow, such as manuscript preparation, reviewer-paper matching, evaluation, and post-review analysis. The authors outline the general steps of the peer review process, from manuscript submission to camera-ready revision, and detail the challenges and opportunities for NLP assistance at each stage. They emphasize the importance of NLP in improving the efficiency and robustness of peer review, while also addressing ethical and practical considerations. Key areas where NLP can provide assistance include: - **Before Review**: Preparing the submission, scoring potential reviewer-paper matches, and matching reviewers. - **During Review**: Evaluating manuscripts, writing review reports, and facilitating discussions. - **After Review**: Performing meta-reviewing, supporting decision-making, and facilitating post-review analysis. The paper also highlights the need for addressing specific challenges, such as data acquisition, ethical issues, and operationalization. To support community efforts, the authors create a companion repository that aggregates key datasets related to peer review. Finally, the paper calls for action from the scientific community, NLP and AI researchers, policymakers, and funding bodies to advance the research in NLP for peer review, aiming to improve the overall quality control in scientific publications.The paper discusses the potential of Natural Language Processing (NLP) to enhance the peer review process, which is crucial for ensuring the quality and integrity of scientific publications. As the volume of scientific articles increases, the traditional peer review process becomes increasingly challenging due to its complexity, time consumption, and susceptibility to errors. NLP has the potential to address these issues by automating various aspects of the peer review workflow, such as manuscript preparation, reviewer-paper matching, evaluation, and post-review analysis. The authors outline the general steps of the peer review process, from manuscript submission to camera-ready revision, and detail the challenges and opportunities for NLP assistance at each stage. They emphasize the importance of NLP in improving the efficiency and robustness of peer review, while also addressing ethical and practical considerations. Key areas where NLP can provide assistance include: - **Before Review**: Preparing the submission, scoring potential reviewer-paper matches, and matching reviewers. - **During Review**: Evaluating manuscripts, writing review reports, and facilitating discussions. - **After Review**: Performing meta-reviewing, supporting decision-making, and facilitating post-review analysis. The paper also highlights the need for addressing specific challenges, such as data acquisition, ethical issues, and operationalization. To support community efforts, the authors create a companion repository that aggregates key datasets related to peer review. Finally, the paper calls for action from the scientific community, NLP and AI researchers, policymakers, and funding bodies to advance the research in NLP for peer review, aiming to improve the overall quality control in scientific publications.