Story Ribbons: Reimagining Storyline Visualizations with Large Language Models

TL;DR

This paper presents Story Ribbons, an interactive visualization system that leverages large language models to automatically extract and visualize complex narrative relationships in literature, aiding both novice and expert analysts.

Contribution

It introduces an LLM-driven data parsing pipeline and a novel visualization system for exploring character and theme trajectories in stories, enhancing narrative analysis tools.

Findings

LLMs effectively extract narrative information from literary texts.

Story Ribbons enables detailed exploration of character and theme trajectories.

User studies show improved analysis efficiency and new insights.

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.