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Peer review analyze: A novel benchmark resource for computational analysis of peer reviews

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  • Tirthankar Ghosal
  • Sandeep Kumar
  • Prabhat Kumar Bharti
  • Asif Ekbal

Abstract

Peer Review is at the heart of scholarly communications and the cornerstone of scientific publishing. However, academia often criticizes the peer review system as non-transparent, biased, arbitrary, a flawed process at the heart of science, leading to researchers arguing with its reliability and quality. These problems could also be due to the lack of studies with the peer-review texts for various proprietary and confidentiality clauses. Peer review texts could serve as a rich source of Natural Language Processing (NLP) research on understanding the scholarly communication landscape, and thereby build systems towards mitigating those pertinent problems. In this work, we present a first of its kind multi-layered dataset of 1199 open peer review texts manually annotated at the sentence level (∼ 17k sentences) across the four layers, viz. Paper Section Correspondence, Paper Aspect Category, Review Functionality, and Review Significance. Given a text written by the reviewer, we annotate: to which sections (e.g., Methodology, Experiments, etc.), what aspects (e.g., Originality/Novelty, Empirical/Theoretical Soundness, etc.) of the paper does the review text correspond to, what is the role played by the review text (e.g., appreciation, criticism, summary, etc.), and the importance of the review statement (major, minor, general) within the review. We also annotate the sentiment of the reviewer (positive, negative, neutral) for the first two layers to judge the reviewer’s perspective on the different sections and aspects of the paper. We further introduce four novel tasks with this dataset, which could serve as an indicator of the exhaustiveness of a peer review and can be a step towards the automatic judgment of review quality. We also present baseline experiments and results for the different tasks for further investigations. We believe our dataset would provide a benchmark experimental testbed for automated systems to leverage on current NLP state-of-the-art techniques to address different issues with peer review quality, thereby ushering increased transparency and trust on the holy grail of scientific research validation. Our dataset and associated codes are available at https://www.iitp.ac.in/~ai-nlp-ml/resources.html#Peer-Review-Analyze.

Suggested Citation

  • Tirthankar Ghosal & Sandeep Kumar & Prabhat Kumar Bharti & Asif Ekbal, 2022. "Peer review analyze: A novel benchmark resource for computational analysis of peer reviews," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-29, January.
    • Handle: RePEc:plo:pone00:0259238
    DOI: 10.1371/journal.pone.0259238
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    References listed on IDEAS

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    1. Giangiacomo Bravo & Francisco Grimaldo & Emilia López-Iñesta & Bahar Mehmani & Flaminio Squazzoni, 2019. "The effect of publishing peer review reports on referee behavior in five scholarly journals," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    2. Elise S. Brezis & Aliaksandr Birukou, 2020. "Arbitrariness in the peer review process," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 393-411, April.
    3. Carole J. Lee & Cassidy R. Sugimoto & Guo Zhang & Blaise Cronin, 2013. "Bias in peer review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 2-17, January.
    4. Heidi Ledford & Richard Van Noorden, 2020. "High-profile coronavirus retractions raise concerns about data oversight," Nature, Nature, vol. 582(7811), pages 160-160, June.
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    Cited by:

    1. Buljan, Ivan & Garcia-Costa, Daniel & Grimaldo, Francisco & Klein, Richard A. & Bakker, Marjan & Marušić, Ana, 2024. "Development and application of a comprehensive glossary for the identification of statistical and methodological concepts in peer review reports," Journal of Informetrics, Elsevier, vol. 18(3).
    2. Wenqing Wu & Haixu Xi & Chengzhi Zhang, 2024. "Are the confidence scores of reviewers consistent with the review content? Evidence from top conference proceedings in AI," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4109-4135, July.
    3. Gabriel Okasa & Alberto de Le'on & Michaela Strinzel & Anne Jorstad & Katrin Milzow & Matthias Egger & Stefan Muller, 2024. "A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports," Papers 2411.16662, arXiv.org, revised Dec 2024.

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