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    <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.</abstract>
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%0 Conference Proceedings
%T Collaborative Document Simplification Using Multi-Agent Systems
%A Fang, Dengzhao
%A Qiang, Jipeng
%A Ouyang, Xiaoye
%A Zhu, Yi
%A Yuan, Yunhao
%A Li, Yun
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F fang-etal-2025-collaborative
%X 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.
%U https://aclanthology.org/2025.coling-main.60/
%P 897-912

Markdown (Informal)

[Collaborative Document Simplification Using Multi-Agent Systems](https://aclanthology.org/2025.coling-main.60/) (Fang et al., COLING 2025)

• Collaborative Document Simplification Using Multi-Agent Systems (Fang et al., COLING 2025)

ACL

• Dengzhao Fang, Jipeng Qiang, Xiaoye Ouyang, Yi Zhu, Yunhao Yuan, and Yun Li. 2025. Collaborative Document Simplification Using Multi-Agent Systems. In Proceedings of the 31st International Conference on Computational Linguistics, pages 897–912, Abu Dhabi, UAE. Association for Computational Linguistics.