UniT: One Document, Many Revisions, Too Many Edit Intention Taxonomies

Fangping Lan, Abdullah Aljebreen, Eduard Dragut


Abstract
Writing is inherently iterative, each revision enhancing information representation. One revision may contain many edits. Examination of the intentions behind edits provides valuable insights into an editor’s expertise, the dynamics of collaborative writing, and the evolution of a document. Current research on edit intentions lacks a comprehensive edit intention taxonomy (EIT) that spans multiple application domains. As a result, researchers often create new EITs tailored to specific needs, a process that is both time-consuming and costly. To address this gap, we propose UniT, a Unified edit intention Taxonomy that integrates existing EITs encompassing a wide range of edit intentions. We examine the lineage relationship and the construction of 24 EITs. They together have 232 categories across various domains. During the literature survey and integration process, we identify challenges such as one-to-many category matches, incomplete definitions, and varying hierarchical structures. We propose solutions for resolving these issues. Finally, our evaluation shows that our UniT achieves higher inter-annotator agreement scores compared to existing EITs and is applicable to a large set of application domains.
Anthology ID:
2025.findings-acl.1180
Volume:
Findings of the Association for Computational Linguistics: ACL 2025
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Venues:
Findings | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
23005–23024
Language:
URL:
https://preview.aclanthology.org/acl25-workshop-ingestion/2025.findings-acl.1180/
DOI:
Bibkey:
Cite (ACL):
Fangping Lan, Abdullah Aljebreen, and Eduard Dragut. 2025. UniT: One Document, Many Revisions, Too Many Edit Intention Taxonomies. In Findings of the Association for Computational Linguistics: ACL 2025, pages 23005–23024, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
UniT: One Document, Many Revisions, Too Many Edit Intention Taxonomies (Lan et al., Findings 2025)
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PDF:
https://preview.aclanthology.org/acl25-workshop-ingestion/2025.findings-acl.1180.pdf