Story Ribbons: Reimagining Storyline Visualizations with Large Language Models

2025-08-08

AI Summary

Narrative structure in literary analysis remains challenging to quantify and model systematically. Method: This study proposes an LLM-driven automated narrative parsing and visualization framework. It introduces an end-to-end LLM-based information extraction pipeline to precisely identify fine-grained interactions among characters, locations, and themes in novels and scripts. It innovatively redefines story-line visualization by designing an interactive system supporting dynamic, cross-level (macro-plot to micro-event) and multi-scale (temporal, spatial, semantic) exploration, augmented with human-in-the-loop mechanisms to mitigate LLM uncertainty. Contribution/Results: Evaluated on 36 canonical literary works, the method significantly improves both efficiency and accuracy in generating structured narrative data. It uncovers latent relational patterns and structural motifs overlooked by traditional close reading, thereby establishing a scalable, interpretable analytical infrastructure for digital humanities research.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Extracting structured narrative data from unstructured stories
• Enhancing storyline visualization with large language models
• Exploring character and theme trajectories interactively

Innovation

• LLM-driven pipeline extracts narrative data
• Interactive Story Ribbons visualize trajectories
• User studies validate LLM narrative insights