AI4Reading: Chinese Audiobook Interpretation System Based on Multi-Agent Collaboration

2025-12-29

AI Summary

To address the low efficiency and high cost of manually producing Chinese audiobook commentary content, this paper proposes the first multi-agent collaborative generation system tailored for podcast-style audiobook interpretation. The method introduces a novel framework comprising 11 specialized agents, covering the end-to-end pipeline—from thematic mining and illustrative case extraction to logical structuring and colloquial script synthesis. It tightly integrates large language models (LLMs) with text-to-speech (TTS) technologies, incorporating modules for thematic analysis, case-based reasoning, editorial refinement, iterative factual verification, and speech synthesis. Experimental results demonstrate that the generated commentary scripts significantly outperform human-expert versions in conciseness and factual accuracy, though speech naturalness remains an area for improvement. This work establishes a new paradigm for high-quality, scalable automation of spoken-content production.

Technology Category

• Natural Language Processing: Generation
• Multiagent Systems: Coordination and Collaboration
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Automates creation of Chinese audiobook interpretations
• Reduces manual effort in producing book analyses
• Enhances content accuracy and narrative structure

Innovation

• Multi-agent collaboration with specialized roles
• Leveraging LLMs for content analysis and generation
• Integrating speech synthesis for podcast-style output