@inproceedings{huang-etal-2025-ai4reading,
title = "{AI}4{R}eading: {C}hinese Audiobook Interpretation System Based on Multi-Agent Collaboration",
author = "Huang, Minjiang and
Qiang, Jipeng and
Zhu, Yi and
Zhang, Chaowei and
Zhao, Xiangyu and
Yu, Kui",
editor = "Mishra, Pushkar and
Muresan, Smaranda and
Yu, Tao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-demo.21/",
doi = "10.18653/v1/2025.acl-demo.21",
pages = "211--220",
ISBN = "979-8-89176-253-4",
abstract = "Audiobook interpretations are attracting increasing attention, as they provide accessible and in-depth analyses of books that offer readers practical insights and intellectual inspiration. However, their manual creation process remains time-consuming and resource-intensive. To address this challenge, we propose AI4Reading, a multi-agent collaboration system leveraging large language models (LLMs) and speech synthesis technology to generate podcast-like audiobook interpretations. The system is designed to meet three key objectives: accurate content preservation, enhanced comprehensibility, and a logical narrative structure. To achieve these goals, We develop a framework composed of 11 specialized agents{---}including topic analysts, case analysts, editors, a narrator, and proofreaders{---}that work in concert to explore themes, extract real-world cases, refine content organization, and synthesize natural spoken language. By comparing expert interpretations with our system{'}s output, the results show that although AI4Reading still has a gap in speech generation quality, the generated interpretative scripts are simpler and more accurate. The code of AI4Reading is publicly accessible , with a demonstration video available ."
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<abstract>Audiobook interpretations are attracting increasing attention, as they provide accessible and in-depth analyses of books that offer readers practical insights and intellectual inspiration. However, their manual creation process remains time-consuming and resource-intensive. To address this challenge, we propose AI4Reading, a multi-agent collaboration system leveraging large language models (LLMs) and speech synthesis technology to generate podcast-like audiobook interpretations. The system is designed to meet three key objectives: accurate content preservation, enhanced comprehensibility, and a logical narrative structure. To achieve these goals, We develop a framework composed of 11 specialized agents—including topic analysts, case analysts, editors, a narrator, and proofreaders—that work in concert to explore themes, extract real-world cases, refine content organization, and synthesize natural spoken language. By comparing expert interpretations with our system’s output, the results show that although AI4Reading still has a gap in speech generation quality, the generated interpretative scripts are simpler and more accurate. The code of AI4Reading is publicly accessible , with a demonstration video available .</abstract>
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%0 Conference Proceedings
%T AI4Reading: Chinese Audiobook Interpretation System Based on Multi-Agent Collaboration
%A Huang, Minjiang
%A Qiang, Jipeng
%A Zhu, Yi
%A Zhang, Chaowei
%A Zhao, Xiangyu
%A Yu, Kui
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F huang-etal-2025-ai4reading
%X Audiobook interpretations are attracting increasing attention, as they provide accessible and in-depth analyses of books that offer readers practical insights and intellectual inspiration. However, their manual creation process remains time-consuming and resource-intensive. To address this challenge, we propose AI4Reading, a multi-agent collaboration system leveraging large language models (LLMs) and speech synthesis technology to generate podcast-like audiobook interpretations. The system is designed to meet three key objectives: accurate content preservation, enhanced comprehensibility, and a logical narrative structure. To achieve these goals, We develop a framework composed of 11 specialized agents—including topic analysts, case analysts, editors, a narrator, and proofreaders—that work in concert to explore themes, extract real-world cases, refine content organization, and synthesize natural spoken language. By comparing expert interpretations with our system’s output, the results show that although AI4Reading still has a gap in speech generation quality, the generated interpretative scripts are simpler and more accurate. The code of AI4Reading is publicly accessible , with a demonstration video available .
%R 10.18653/v1/2025.acl-demo.21
%U https://aclanthology.org/2025.acl-demo.21/
%U https://doi.org/10.18653/v1/2025.acl-demo.21
%P 211-220
Markdown (Informal)
[AI4Reading: Chinese Audiobook Interpretation System Based on Multi-Agent Collaboration](https://aclanthology.org/2025.acl-demo.21/) (Huang et al., ACL 2025)
ACL