@inproceedings{jiang-etal-2025-archidocgen,
title = "{A}rchi{D}oc{G}en: Multi-Agent Framework for Expository Document Generation in the Architectural Industry",
author = "Jiang, Junjie and
Wu, Haodong and
Zhang, Yongqi and
Guo, Songyue and
Liu, Bingcen and
Cao, Caleb Chen and
Shao, Ruizhe and
Guan, Chao and
Xu, Peng and
Chen, Lei",
editor = "Rehm, Georg and
Li, Yunyao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-industry.43/",
doi = "10.18653/v1/2025.acl-industry.43",
pages = "605--618",
ISBN = "979-8-89176-288-6",
abstract = "The architectural industry produces extensive documents, including method statements{---}expository documents that integrate multi-source data into actionable guidance. Manual drafting however is labor-intensive and time-consuming. This paper introduces ArchiDocGen, a multi-agent framework automating method statement generation. Unlike traditional approaches relying on static templates or single-pass generation, ArchiDocGen decomposes the task into three steps: outline generation, section-based content generation, and polishing, each handled by specialized agents. To provide domain expertise, ArchiDocGen employs a section-based retriever to fetch and synthesize relevant documents from its custom knowledge base. Each section is generated through iterative reasoning of a section-based chain-of-thought (SeCoT) scheme, followed by refinement to meet professional standards. To evaluate the generated method statements, we partner with the industry to establish a multi-dimensional evaluation system by combining automatic and empirical methods. Experiments show that ArchiDocGen achieves 4.38 ContentScore, excelling in specialization, completeness, organization, and clarity. Additionally, a web-based application for ArchiDocGen is developed and deployed with industry partners."
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<abstract>The architectural industry produces extensive documents, including method statements—expository documents that integrate multi-source data into actionable guidance. Manual drafting however is labor-intensive and time-consuming. This paper introduces ArchiDocGen, a multi-agent framework automating method statement generation. Unlike traditional approaches relying on static templates or single-pass generation, ArchiDocGen decomposes the task into three steps: outline generation, section-based content generation, and polishing, each handled by specialized agents. To provide domain expertise, ArchiDocGen employs a section-based retriever to fetch and synthesize relevant documents from its custom knowledge base. Each section is generated through iterative reasoning of a section-based chain-of-thought (SeCoT) scheme, followed by refinement to meet professional standards. To evaluate the generated method statements, we partner with the industry to establish a multi-dimensional evaluation system by combining automatic and empirical methods. Experiments show that ArchiDocGen achieves 4.38 ContentScore, excelling in specialization, completeness, organization, and clarity. Additionally, a web-based application for ArchiDocGen is developed and deployed with industry partners.</abstract>
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%0 Conference Proceedings
%T ArchiDocGen: Multi-Agent Framework for Expository Document Generation in the Architectural Industry
%A Jiang, Junjie
%A Wu, Haodong
%A Zhang, Yongqi
%A Guo, Songyue
%A Liu, Bingcen
%A Cao, Caleb Chen
%A Shao, Ruizhe
%A Guan, Chao
%A Xu, Peng
%A Chen, Lei
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F jiang-etal-2025-archidocgen
%X The architectural industry produces extensive documents, including method statements—expository documents that integrate multi-source data into actionable guidance. Manual drafting however is labor-intensive and time-consuming. This paper introduces ArchiDocGen, a multi-agent framework automating method statement generation. Unlike traditional approaches relying on static templates or single-pass generation, ArchiDocGen decomposes the task into three steps: outline generation, section-based content generation, and polishing, each handled by specialized agents. To provide domain expertise, ArchiDocGen employs a section-based retriever to fetch and synthesize relevant documents from its custom knowledge base. Each section is generated through iterative reasoning of a section-based chain-of-thought (SeCoT) scheme, followed by refinement to meet professional standards. To evaluate the generated method statements, we partner with the industry to establish a multi-dimensional evaluation system by combining automatic and empirical methods. Experiments show that ArchiDocGen achieves 4.38 ContentScore, excelling in specialization, completeness, organization, and clarity. Additionally, a web-based application for ArchiDocGen is developed and deployed with industry partners.
%R 10.18653/v1/2025.acl-industry.43
%U https://aclanthology.org/2025.acl-industry.43/
%U https://doi.org/10.18653/v1/2025.acl-industry.43
%P 605-618
Markdown (Informal)
[ArchiDocGen: Multi-Agent Framework for Expository Document Generation in the Architectural Industry](https://aclanthology.org/2025.acl-industry.43/) (Jiang et al., ACL 2025)
ACL
- Junjie Jiang, Haodong Wu, Yongqi Zhang, Songyue Guo, Bingcen Liu, Caleb Chen Cao, Ruizhe Shao, Chao Guan, Peng Xu, and Lei Chen. 2025. ArchiDocGen: Multi-Agent Framework for Expository Document Generation in the Architectural Industry. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 605–618, Vienna, Austria. Association for Computational Linguistics.