# Making Revisions Understandable: A Survey of Edit Intentions, Methods, and Applications

arXiv: https://arxiv.org/abs/2609.01610 · HTML: https://mlsanity.com/paper/2609.01610/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2026.

## Built on by (most cited first, 1 of 1)

- [From Granular Revision Operations to Meaningful Revision Units: Evaluating LLMs for Revision Boundary Detection](https://mlsanity.com/paper/2609.33720/) (2026)

## Abstract (arXiv)

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.