# Story Ribbons: Reimagining Storyline Visualizations with Large Language Models

arXiv: https://arxiv.org/abs/2508.06772 · HTML: https://mlsanity.com/paper/2508.06772/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 77 references that are in the map.
- First posted on arXiv 9 Aug 2025.

## Builds on (most cited first, 29 of 29)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking](https://mlsanity.com/paper/2004.14967/) (2020)
- [LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models](https://mlsanity.com/paper/2303.02927/) (2023)
- [NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries](https://mlsanity.com/paper/2008.10723/) (2020)
- [FABLES: Evaluating faithfulness and content selection in book-length summarization](https://mlsanity.com/paper/2404.01261/) (2024)
- [DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing](https://mlsanity.com/paper/2401.10880/) (2024)
- [AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization](https://mlsanity.com/paper/2102.01330/) (2021)
- [Foundation Models Meet Visualizations: Challenges and Opportunities](https://mlsanity.com/paper/2310.05771/) (2023)
- [CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis](https://mlsanity.com/paper/2406.12665/) (2024)
- [AgentLens: Visual Analysis for Agent Behaviors in LLM-based Autonomous Systems](https://mlsanity.com/paper/2402.08995/) (2024)
- [Patchview: LLM-Powered Worldbuilding with Generative Dust and Magnet Visualization](https://mlsanity.com/paper/2408.04112/) (2024)
- [Designing LLM Chains by Adapting Techniques from Crowdsourcing Workflows](https://mlsanity.com/paper/2312.11681/) (2023)
- [HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs](https://mlsanity.com/paper/2405.17633/) (2024)
- [Salience-Aware Event Chain Modeling for Narrative Understanding](https://mlsanity.com/paper/2109.10475/) (2021)
- [Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing](https://mlsanity.com/paper/2306.02052/) (2023)
- [Visual Story-Writing: Writing by Manipulating Visual Representations of Stories](https://mlsanity.com/paper/2410.07486/) (2024)
- [PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning](https://mlsanity.com/paper/2009.00249/) (2020)
- [Portrayal: Leveraging NLP and Visualization for Analyzing Fictional Characters](https://mlsanity.com/paper/2308.04056/) (2023)
- [Modeling Event Salience in Narratives via Barthes' Cardinal Functions](https://mlsanity.com/paper/2011.01785/) (2020)
- [RAGViz: Diagnose and Visualize Retrieval-Augmented Generation](https://mlsanity.com/paper/2411.01751/) (2024)
- [Mapping Out Narrative Structures and Dynamics Using Networks and Textual Information](https://mlsanity.com/paper/1604.03029/) (2016)
- [Analyzing Nobel Prize Literature with Large Language Models](https://mlsanity.com/paper/2410.18142/) (2024)
- [Improving Quotation Attribution with Fictional Character Embeddings](https://mlsanity.com/paper/2406.11368/) (2024)
- [Show, Don't Tell: Uncovering Implicit Character Portrayal using LLMs](https://mlsanity.com/paper/2412.04576/) (2024)
- [Improving Tag-Clouds as Visual Information Retrieval Interfaces](https://mlsanity.com/paper/2401.04947/) (2024)
- [Storyline Visualizations with Ubiquitous Actors](https://mlsanity.com/paper/2008.04125/) (2020)
- [StoryExplorer: A Visualization Framework for Storyline Generation of Textual Narratives](https://mlsanity.com/paper/2411.05435/) (2024)

## Built on by (most cited first, 1 of 1)

- [FocalLens: Visualizing Narratives through Focalization](https://mlsanity.com/paper/2604.14456/) (2026)

## Abstract (arXiv)

Analyzing literature involves tracking interactions between characters, locations, and themes. Visualization has the potential to facilitate the mapping and analysis of these complex relationships, but capturing structured information from unstructured story data remains a challenge. As large language models (LLMs) continue to advance, we see an opportunity to use their text processing and analysis capabilities to augment and reimagine existing storyline visualization techniques. Toward this goal, we introduce an LLM-driven data parsing pipeline that automatically extracts relevant narrative information from novels and scripts. We then apply this pipeline to create Story Ribbons, an interactive visualization system that helps novice and expert literary analysts explore detailed character and theme trajectories at multiple narrative levels. Through pipeline evaluations and user studies with Story Ribbons on 36 literary works, we demonstrate the potential of LLMs to streamline narrative visualization creation and reveal new insights about familiar stories. We also describe current limitations of AI-based systems, and interaction motifs designed to address these issues.