Revise and Resubmit
An Intertextual Model of Text-based Collaboration in Peer Review
Bibliographic Data
| ID | 12155776 |
|---|---|
| Authors | Ilia Kuznetsov (0000-0002-6359-2774, UKP Lab Technical University of Darmstadt, Department of Computer Science. https://www.ukp.tu-darmstadt.de, corresponding author), Jan P Buchmann (0000-0002-6842-1229, Technische Universität Darmstadt), Jan Buchmann (Technical University of Darmstadt, Department of Computer Science UKP Lab), Max Eichler (Technical University of Darmstadt, Department of Computer Science UKP Lab), Iryna Gurevych (0000-0003-2187-7621, Technical University of Darmstadt, Department of Computer Science UKP Lab) |
| Year | 2022 |
| Volume | 48 |
| Issue | 4 |
| Pages | 949-986 |
| Publication date | 2022-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Computational Linguistics (JOURNAL) |
| Journal identifiers | ISSN: 0891-2017 • E-ISSN: 1530-9312 |
| Publisher | Association for Computational Linguistics (PUBLISHER • US) |
| DOI | 10.1162/coli_a_00455 |
| OpenAlex | W4290774620 |
| Language | EN |
| Citations received | 1 |
| References cited | 47 |
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.1
Annotation · Data science · Focus (optics · Information retrieval · Intertextuality · Linguistics · Operationalization · Resource (disambiguation · Social media · Topic model · World Wide Web · Computer Science · Expert finding and Q&A systems · Software Engineering Research · Topic Modeling · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,33 |
| Citation span | 2023 - 2023 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |