NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization

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. * Equal contribution. Preprocessor ( ) Narrative ( ) Preprocessed Narrative ( ) Chunking Concat Chunking Concat Narrative Summarizer ( ) Initial Summary ( ) Chunking Compressor ( ) Stage 1. Preprocessing Stage 2. Narrative Summarization Initial Summary ( ) Concat Summary 1 ( ) Compressor ( ) Summary ( )