Challenge: CASIMIR dataset contains multiple revisions of 15,646 scientific articles . authors question the relevance of current evaluation methods for text revision .
Approach: They propose a textual resource on the revision step of the writing process of scientific articles.
Outcome: The proposed dataset contains the multiple revised versions of 15,646 scientific articles from OpenReview, along with their peer reviews.

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arXivEdits: Understanding the Human Revision Process in Scientific Writing (2022.emnlp-main)

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Challenge: a new computational framework is developed to study text revision in scientific writing . authors propose a method to extract revision at document-, sentence-, and word-levels .
Approach: They propose a computational framework for studying text revision in scientific writing . arXivEdits is an annotated corpus of 751 full papers from arX . authors propose to use sentence alignment, fine-grained edits and intents to extract revision .
Outcome: The proposed framework can be used to study revision in scientific writing.
A Multi-level Annotated Corpus of Scientific Papers for Scientific Document Summarization and Cross-document Relation Discovery (2020.lrec-1)

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Challenge: Recent studies have proposed to take advantage of the scientific paper's citation network to approach literature summarization.
Approach: They propose to annotate related work sections, cite papers and sentences using machine readable data and an additional layer of papers citing the references.
Outcome: The proposed corpus expands the existing data-set of related work sections and cites the papers cited in the related work section.
Making Revisions Understandable: A Survey of Edit Intentions, Methods, and Applications (2026.findings-acl)

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Challenge: Text revision is a core process in document creation, capturing how authors iteratively refine, reorganize, and improve written content.
Approach: They synthesize text revision research through the lens of edit intentions . they review prior work across the revision workflow including corpus construction, edit intention taxonomies, edit intentions, and edit intention identification.
Outcome: The proposed approach synthesizes datasets, taxonomies, identification methods, and applications and highlights key open research directions.
Re3: A Holistic Framework and Dataset for Modeling Collaborative Document Revision (2024.acl-long)

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Challenge: a framework for collaborative document revision is lacking for empirical analysis and NLP.
Approach: They propose a framework for joint analysis of collaborative document revision that instantiates a corpus of aligned scientific paper revisions manually labeled according to their action and intent.
Outcome: The proposed framework provides first empirical insights into collaborative document revision in the academic domain and assesses its capabilities.
Understanding Iterative Revision from Human-Written Text (2022.acl-long)

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Challenge: This work describes IteraTeR: the first large-scale, multi-domain, edit-intention annotated corpus of iteratively revised text.
Approach: They propose to annotate iteratively revised text using a multi-domain annotated corpus that generalizes to a variety of domains, edit intentions, revision depths, and granularities.
Outcome: The proposed model improves automatic evaluations by integrating edit intentions with writing quality.
One Document, Many Revisions: A Dataset for Classification and Description of Edit Intents (2022.lrec-1)

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Challenge: Existing methods to understand revisions have failed to provide a deeper understanding of the nature of these edits.
Approach: They propose to use a Wikipedia revision history dataset to train a classifier that achieves a 90% accuracy in identifying edit intent and a distantly-supervised model that generates .
Outcome: The proposed model achieves 90% accuracy in identifying edit intent and a best score of 28 ROUGE.
Identifying Reliable Evaluation Metrics for Scientific Text Revision (2025.acl-long)

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Challenge: Effective revision is a critical step in scientific writing, ensuring clarity, coherence, and adherence to academic standards.
Approach: They propose to use ROUGE and BERTScore to assess revision quality . they also examine LLM-as-a-judge approaches to assess instruction-following revisions .
Outcome: The proposed method improves the accuracy of revision tasks with and without a gold reference.
WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse (D18-1)

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Challenge: a corpus of 43 million atomic edits is available for Wikipedia edit history . edits are instances in which a human editor has inserted a single contiguous phrase into, or deleted a contigous phrase from, an existing sentence.
Approach: They use Wikipedia edit history to mine atomic edits across 8 languages . they find edits contain instances in which a human editor has inserted a single phrase into, or deleted a contiguous phrase from, an existing sentence.
Outcome: The data show that edits differ from the language observed in standard corpora and that models trained on edits encode different aspects of semantics and discourse than models trained in raw text.
ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews (2024.acl-long)

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Challenge: Existing systems that can interpret complex writing feedback and edit documents in response are limited on the most demanding writing tasks.
Approach: They propose to use peer feedback to revise scientific papers based on peer feedback . they provide labels linking each reviewer comment to the specific paper edits made by the author .
Outcome: The proposed model fails to identify which edits correspond to a comment and the original paper.
Beyond Metadata: What Paper Authors Say About Corpora They Use (2021.findings-acl)

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Challenge: Currently, dataset retrieval relies almost exclusively on metadata provided by the publishers.
Approach: They propose to use metadata to extract review statements from scientific publications . they argue that a crucial piece of information is missing to inform the examination of search results .
Outcome: The proposed analysis is the first of its kind in the field of Natural Language Processing.

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