NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization
Hyuntak Kim, Byung‐Hak Kim · 2025
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 multiagent LLMs in handling long-form content, offering a scalable approach for structured summarization in diverse storytelling domains.