用多智能体框架提升长篇叙事摘要质量，无需微调
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization
- 将对话与描述统一转为标准格式，增强情节连贯性
- 分层处理文本块，输出长度可控，准确率提升30%
- 适合需要高质量剧情摘要的影视、文学分析场景
长篇叙事（如书籍、电影、电视剧本）的摘要需捕捉复杂情节、角色互动与主题一致性，现有大模型仍难胜任。本文提出NexusSum，一种无需微调的多智能体大模型框架，通过结构化流水线处理长文本。核心创新包括：(1) 对话转描述转换——将角色对话与描写文本统一为标准格式，提升连贯性；(2) 分层多模型摘要——优化分块处理并控制输出长度，确保高质量摘要。该方法在书籍、电影和电视剧本上均达新最佳，BERTScore（F1）最高提升30.0%。结果表明，多智能体架构在长文本摘要中具有强可扩展性，适用于多样化叙事领域。
原文摘要 · Abstract (English)
Summarizing long-form narratives--such as books, movies, and TV scripts--requires capturing intricate plotlines, character interactions, and thematic coherence, a task that remains challenging for existing LLMs. We introduce NexusSum, a multi-agent LLM framework for narrative summarization that processes long-form text through a structured, sequential pipeline--without requiring fine-tuning. Our approach introduces two key innovations: (1) Dialogue-to-Description Transformation: A narrative-specific preprocessing method that standardizes character dialogue and descriptive text into a unified format, improving coherence. (2) Hierarchical Multi-LLM Summarization: A structured summarization pipeline that optimizes chunk processing and controls output length for accurate, high-quality summaries. Our method establishes a new state-of-the-art in narrative summarization, achieving up to a 30.0% improvement in BERTScore (F1) across books, movies, and TV scripts. These results demonstrate the effectiveness of multi-agent LLMs in handling long-form content, offering a scalable approach for structured summarization in diverse storytelling domains.
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