NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization

• URL: http://arxiv.org/abs/2505.24575v1
• Date: Fri, 30 May 2025 13:26:23 GMT
• Title: NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization
• Authors: Hyuntak Kim, Byung-Hak Kim,
• Abstract summary: We introduce NexusSum, a multi-agent LLM framework for narrative summarization.<n>A narrative-specific preprocessing method standardizes character dialogue and descriptive text into a unified format.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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