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

Source: arXiv - 2512.23300v1

Overview

The paper introduces AI4Reading, a multi‑agent system that combines large language models (LLMs) with speech‑synthesis to automatically produce Chinese audiobook‑style interpretations of books. By orchestrating a team of specialized AI “agents,” the authors aim to cut down the labor‑intensive manual workflow while preserving the depth and clarity of human‑crafted analyses.

Key Contributions

• Multi‑agent collaboration framework: 11 purpose‑built agents (topic analyst, case analyst, editor, narrator, proofreader, etc.) that divide the interpretation pipeline into manageable, parallel tasks.
• Content‑preservation + comprehensibility trade‑off: The system explicitly optimizes for faithful representation of the source material while re‑phrasing it into simpler, listener‑friendly language.
• Narrative‑structure enforcement: An editorial agent reorganizes extracted insights into a logical flow, mimicking the structure of professional podcast scripts.
• End‑to‑end prototype: Integration of LLM‑driven text generation with state‑of‑the‑art Chinese speech synthesis, delivering a complete “read‑aloud” experience.
• Human‑centric evaluation: Comparative study against expert‑written interpretations, showing higher accuracy and readability of AI‑generated scripts (though speech quality still lags behind human narration).

Methodology

1. Document Ingestion – The target book is split into sections and fed to the system.
2. Topic Analyst Agent – Uses an LLM to extract high‑level themes and key questions.
3. Case Analyst Agent – Searches the text (or external knowledge bases) for real‑world examples that illustrate each theme.
4. Content Drafting Agents – Multiple LLM instances rewrite the extracted material into concise, conversational sentences.
5. Editor Agent – Reorders the drafts, adds transitions, and ensures a coherent narrative arc.
6. Proofreader Agent – Checks for factual consistency, redundancy, and language fluency.
7. Narrator Agent – Sends the final script to a Chinese neural TTS (text‑to‑speech) engine, producing the audio file.

All agents communicate through a shared “task board” (a structured JSON format), allowing asynchronous execution and easy debugging. The design mirrors a small editorial team, but each role is automated and can be scaled across many books simultaneously.

Results & Findings

• Script Quality: Human evaluators rated AI4Reading’s scripts as simpler and more factually accurate than those written by domain experts, indicating successful abstraction without losing core meaning.
• Speech Quality: The generated audio was judged acceptable for comprehension but still exhibited unnatural prosody and occasional pronunciation errors compared with professional narrators.
• Efficiency: The end‑to‑end pipeline produced a full‑length interpretation in roughly 30 % of the time required for manual production, demonstrating a clear productivity boost.

Practical Implications

• Rapid Content Repurposing: Publishers can automatically generate companion audio analyses for new releases, expanding accessibility without hiring a full editorial staff.
• Educational Platforms: E‑learning services can enrich textbooks with AI‑driven audio summaries, helping learners who prefer auditory material.
• Podcast Automation: Media companies can spin up “AI‑hosted” discussion episodes for any book, enabling a scalable content pipeline for niche topics.
• Localization: The same multi‑agent architecture can be adapted to other languages, facilitating cross‑market audiobook production with minimal human intervention.

Limitations & Future Work

• Speech Naturalness: Current TTS still produces robotic intonation; the authors suggest integrating expressive prosody models or fine‑tuning on professional narrator data.
• Domain Knowledge Gaps: The case‑analysis agent sometimes pulls irrelevant examples when the source material is highly specialized; future versions could incorporate domain‑specific retrieval APIs.
• Evaluation Scope: Experiments were limited to Chinese texts and a small set of books; broader multilingual benchmarks and larger user studies are needed to validate generalizability.

AI4Reading showcases how a well‑orchestrated suite of LLM‑powered agents can turn dense written works into listener‑friendly audio interpretations, opening the door to faster, more inclusive publishing pipelines.

Authors

• Minjiang Huang
• Jipeng Qiang
• Yi Zhu
• Chaowei Zhang
• Xiangyu Zhao
• Kui Yu

Paper Information

• arXiv ID: 2512.23300v1
• Categories: cs.CL
• Published: December 29, 2025
• PDF: Download PDF