NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization

2025-05-30

AI Summary

Existing large language models (LLMs) struggle to accurately model plot progression, character relationships, and thematic coherence in long-form narrative texts (e.g., novels, films, TV series). To address this, we propose a fine-tuning-free hierarchical multi-agent LLM framework. Our approach introduces a novel “dialogue-to-description” preprocessing mechanism, integrated with narrative-driven text normalization, hierarchical summarization scheduling, and length-controllable generation. By orchestrating multiple LLMs, the framework enables chunk-level optimization and cross-granularity summary synthesis. Evaluated on book, film, and television series datasets, our method achieves a +30.0% improvement in BERTScore (F1) over state-of-the-art methods, substantially advancing the performance ceiling for narrative summarization. Moreover, it demonstrates strong cross-domain generalization capability.

Technology Category

• Natural Language Processing: Summarization
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Multiagent Learning

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Summarizing long-form narratives with complex plots and characters
• Standardizing dialogue and descriptive text for better coherence
• Optimizing chunk processing for accurate, high-quality summaries

Innovation

• Hierarchical multi-agent LLM framework
• Dialogue-to-description transformation preprocessing
• Structured summarization pipeline optimization