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

TL;DR

This paper introduces the first intertextual model for analyzing text-based collaboration in peer review, supported by a multi-domain annotated corpus, advancing NLP applications in editorial review processes.

Contribution

It proposes a novel intertextual framework for peer review analysis and provides the first multi-domain annotated corpus for journal-style post-publication review.

Findings

Developed an intertextual model encompassing tagging, linking, and alignment.

Created the first annotated multi-domain corpus for peer review.

Enabled detailed analysis of text interactions in peer review processes.

Abstract

Peer review is a key component of the publishing process in most fields of science. The 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 multi-domain 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 towards multi-domain, 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. Our corpus and accompanying code are publicly available.