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Revise and Resubmit: An Intertextual Model of Text-Based Collaboration in Peer Review

Kuznetsov, Ilia ; Buchmann, Jan ; Eichler, Max ; Gurevych, Iryna (2024)
Revise and Resubmit: An Intertextual Model of Text-Based Collaboration in Peer Review.
In: Computational Linguistics, 2022, 48 (4)
doi: 10.26083/tuprints-00026489
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Revise and Resubmit: An Intertextual Model of Text-Based Collaboration in Peer Review
Language: English
Date: 10 January 2024
Place of Publication: Darmstadt
Year of primary publication: December 2022
Place of primary publication: Cambridge, MA
Publisher: MIT Press
Journal or Publication Title: Computational Linguistics
Volume of the journal: 48
Issue Number: 4
DOI: 10.26083/tuprints-00026489
Corresponding Links:
Origin: Secondary publication

Peer review is a key component of the publishing process in most fields of science. Increasing submission rates put a strain on reviewing quality and efficiency, motivating the development of applications to support the reviewing and editorial work. While existing NLP studies focus on the analysis of individual texts, editorial assistance often requires modeling interactions between pairs of texts—yet general frameworks and datasets to support this scenario are missing. Relationships between texts are the core object of the intertextuality theory—a family of approaches in literary studies not yet operationalized in NLP. Inspired by prior theoretical work, we propose the first intertextual model of text-based collaboration, which encompasses three major phenomena that make up a full iteration of the review–revise–and–resubmit cycle: pragmatic tagging, linking, and long-document version alignment. While peer review is used across the fields of science and publication formats, existing datasets solely focus on conference-style review in computer science. Addressing this, we instantiate our proposed model in the first annotated multidomain corpus in journal-style post-publication open peer review, and provide detailed insights into the practical aspects of intertextual annotation. Our resource is a major step toward multidomain, fine-grained applications of NLP in editorial support for peer review, and our intertextual framework paves the path for general-purpose modeling of text-based collaboration. We make our corpus, detailed annotation guidelines, and accompanying code publicly available.

Uncontrolled Keywords: NLP, peer review, intertextual, revision, annotation, corpus
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-264893
Additional Information:

Acknowledgement: Funded by the European Union (ERC, INTERTEXT, 101054961). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

Classification DDC: 000 Generalities, computers, information > 004 Computer science
Divisions: 20 Department of Computer Science > Ubiquitous Knowledge Processing
Date Deposited: 10 Jan 2024 07:52
Last Modified: 16 Jan 2024 12:19
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/26489
PPN: 514576944
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