# NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization

arXiv: https://arxiv.org/abs/2505.24575 · HTML: https://mlsanity.com/paper/2505.24575/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 25 of its 38 references that are in the map.
- First posted on arXiv 30 May 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 25 of 25)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [Linformer: Self-Attention with Linear Complexity](https://mlsanity.com/paper/2006.04768/) (2020)
- [Extending Context Window of Large Language Models via Positional Interpolation](https://mlsanity.com/paper/2306.15595/) (2023)
- [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://mlsanity.com/paper/2112.07916/) (2021)
- [Chain of Agents: Large Language Models Collaborating on Long-Context Tasks](https://mlsanity.com/paper/2406.02818/) (2024)
- [BookSum: A Collection of Datasets for Long-form Narrative Summarization](https://mlsanity.com/paper/2105.08209/) (2021)
- [BooookScore: A systematic exploration of book-length summarization in the era of LLMs](https://mlsanity.com/paper/2310.00785/) (2023)
- [Unlimiformer: Long-Range Transformers with Unlimited Length Input](https://mlsanity.com/paper/2305.01625/) (2023)
- [Efficient Long-Text Understanding with Short-Text Models](https://mlsanity.com/paper/2208.00748/) (2022)
- [Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents](https://mlsanity.com/paper/2110.10150/) (2021)
- [Exploring Content Selection in Summarization of Novel Chapters](https://mlsanity.com/paper/2005.01840/) (2020)
- [Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling](https://mlsanity.com/paper/2012.07311/) (2020)
- [A Bag of Tricks for Dialogue Summarization](https://mlsanity.com/paper/2109.08232/) (2021)
- [Select and Summarize: Scene Saliency for Movie Script Summarization](https://mlsanity.com/paper/2404.03561/) (2024)
- [Agent-as-Judge for Factual Summarization of Long Narratives](https://mlsanity.com/paper/2501.09993/) (2025)
- [NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization](https://mlsanity.com/paper/2212.01476/) (2022)
- [End-to-End Long Document Summarization using Gradient Caching](https://mlsanity.com/paper/2501.01805/) (2025)
- [DialogSum Challenge: Results of the Dialogue Summarization Shared Task](https://mlsanity.com/paper/2208.03898/) (2022)
- [RISE: Leveraging Retrieval Techniques for Summarization Evaluation](https://mlsanity.com/paper/2212.08775/) (2022)

## Built on by (most cited first, 10 of 10)

- [Memory in the Age of AI Agents](https://mlsanity.com/paper/2512.13564/) (2025)
- [Repository Intelligence Graph: Deterministic Architectural Map for LLM Code Assistants](https://mlsanity.com/paper/2601.10112/) (2026)
- [Learning to Summarize by Learning to Quiz: Adversarial Agentic Collaboration for Long Document Summarization](https://mlsanity.com/paper/2509.20900/) (2025)
- [Enhancing Long Document Long Form Summarisation with Self-Planning](https://mlsanity.com/paper/2512.17179/) (2025)
- [Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization](https://mlsanity.com/paper/2601.08682/) (2026)
- [CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference](https://mlsanity.com/paper/2602.20732/) (2026)
- [Automated Summarization of Software Documents: An LLM-based Multi-Agent Approach](https://mlsanity.com/paper/2606.24689/) (2026)
- [ThreadSumm: Summarization of Nested Discourse Threads Using Tree of Thoughts](https://mlsanity.com/paper/2604.17648/) (2026)
- [On Improving Faithfulness of Podcasts from Documents](https://mlsanity.com/paper/2607.21961/) (2026)
- [Don't Scroll Back: Missing-Evidence Memory for Streaming Dialogue Summarization](https://mlsanity.com/paper/2608.09043/) (2026)

## Abstract (arXiv)

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.