@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://preview.aclanthology.org/ingestion-acl-25/2025.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 ."
}