# Characterizing Stage-Aware Writing Assistance in Collaborative Document Authoring

arXiv: https://arxiv.org/abs/2008.08165 · HTML: https://mlsanity.com/paper/2008.08165/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [Beyond the Chat: Executable and Verifiable Text-Editing with LLMs](https://mlsanity.com/paper/2309.15337/) (2023)
- [Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wild](https://mlsanity.com/paper/2505.16023/) (2025)
- [Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process](https://mlsanity.com/paper/2504.12488/) (2025)
- [Conversational User-AI Intervention: A Study on Prompt Rewriting for Improved LLM Response Generation](https://mlsanity.com/paper/2503.16789/) (2025)
- [PaperDebugger: A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editing](https://mlsanity.com/paper/2512.02589/) (2025)
- [Comparing Native and Non-native English Speakers' Behaviors in Collaborative Writing through Visual Analytics](https://mlsanity.com/paper/2502.18681/) (2025)

## Abstract (arXiv)

Writing is a complex non-linear process that begins with a mental model of intent, and progresses through an outline of ideas, to words on paper (and their subsequent refinement). Despite past research in understanding writing, Web-scale consumer and enterprise collaborative digital writing environments are yet to greatly benefit from intelligent systems that understand the stages of document evolution, providing opportune assistance based on authors' situated actions and context. In this paper, we present three studies that explore temporal stages of document authoring. We first survey information workers at a large technology company about their writing habits and preferences, concluding that writers do in fact conceptually progress through several distinct phases while authoring documents. We also explore, qualitatively, how writing stages are linked to document lifespan. We supplement these qualitative findings with an analysis of the longitudinal user interaction logs of a popular digital writing platform over several million documents. Finally, as a first step towards facilitating an intelligent digital writing assistant, we conduct a preliminary investigation into the utility of user interaction log data for predicting the temporal stage of a document. Our results support the benefit of tools tailored to writing stages, identify primary tasks associated with these stages, and show that it is possible to predict stages from anonymous interaction logs. Together, these results argue for the benefit and feasibility of more tailored digital writing assistance.