Collaborative Document Simplification Using Multi-Agent Systems
Dengzhao Fang, Jipeng Qiang, Xiaoye Ouyang, Yi Zhu, Yunhao Yuan, Yun Li
Abstract
Research on text simplification has been ongoing for many years. However, the task of document simplification (DS) remains a significant challenge due to the need to consider complex factors such as technical terminology, metaphors, and overall coherence. In this work, we introduce a novel multi-agent framework for document simplification (AgentSimp) based on large language models (LLMs). This framework emulates the collaborative process of a human expert team through the roles played by multiple agents, addressing the intricate demands of document simplification. We explore two communication strategies among agents (pipeline-style and synchronous) and two document reconstruction strategies (Direct and Iterative ). According to both automatic evaluation metrics and human evaluation results, the documents simplified by AgentSimp are deemed to be more thoroughly simplified and more coherent on a variety of articles across different types and styles.
BibTeX
@inproceedings{fang-etal-2025-collaborative,
title = "Collaborative Document Simplification Using Multi-Agent Systems",
author = "Fang, Dengzhao and
Qiang, Jipeng and
Ouyang, Xiaoye and
Zhu, Yi and
Yuan, Yunhao and
Li, Yun",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.60/",
pages = "897--912"
}