让修订可理解：编辑意图、方法与应用综述
Making Revisions Understandable: A Survey of Edit Intentions, Methods, and Applications
浏览论文内容
中文总结 AI 辅助
该综述从编辑意图视角整合文本修订研究，梳理了相关数据集、方法与应用，明确了该领域的开放研究方向。
中文摘要 AI 辅助
文本修订是文档创建的核心过程，记录作者如何迭代式地优化、重组和改进书面内容。随着维基百科、arXiv等平台大规模修订历史的可用性不断提升，自然语言处理（NLP）研究已开始超越对“发生了什么变化”的建模，转向理解“为何发生变化”，即潜在的编辑意图。据我们所知，这是首个从编辑意图视角整合文本修订研究的综述，提供了数据集、分类法、识别方法及应用的统一视角。我们回顾了涵盖完整修订流程的过往研究，包括修订语料库构建、编辑意图分类法设计与编辑意图识别；进一步对代表性数据集和方法进行分类，总结了写作辅助、文档修订摘要等下游应用，并强调了关键的开放研究方向。
英文摘要
Text revision is a core process in document creation, capturing how authors iteratively refine, reorganize, and improve written content. With the increasing availability of large-scale revision histories from platforms such as Wikipedia and arXiv, NLP research has begun to move beyond modeling what changes are made to understanding why they are made, i.e., the underlying edit intentions. To our knowledge, this is the first survey that synthesizes text revision research through the lens of edit intentions, providing a unified view of datasets, taxonomies, identification methods, and applications. We review prior work across the full revision workflow, including revision corpus construction, edit intention taxonomy design, and edit intention identification. We further categorize representative datasets and methods, summarize downstream applications such as writing assistance and document edit summarization, and highlight key open research directions.
发表机构
- Temple University（天普大学）
机构由 AI 辅助整理，请以论文原文为准。
