用大模型自动生成故事脉络可视化，帮读者轻松追踪角色与主题演变。
Story Ribbons: Reimagining Storyline Visualizations with Large Language Models
- 用大模型自动解析小说脚本，提取角色、地点、主题等叙事信息。
- 在36部文学作品上验证，能清晰展现多层级的故事轨迹变化。
- 适合文学研究者与初学者，提升分析效率并发现新洞察。
文学分析需追踪角色、场景与主题间的互动关系。可视化有助于揭示这些复杂关联，但从非结构化文本中提取结构化信息仍是难题。随着大语言模型（LLMs）的发展，我们利用其文本处理能力，构建了一套端到端的自动化数据解析流程，可从小说和剧本中自动提取关键叙事要素。基于此，我们开发了「Story Ribbons」——一个交互式可视化系统，支持新手与专家在多个叙事层级上探索角色与主题的动态演变。通过在36部文学作品上的管道评估与用户研究，我们验证了该方法在简化叙事可视化生成过程方面的潜力，并揭示了经典故事的新视角。同时，论文也指出当前AI系统的局限性，并提出针对性的交互设计策略以应对挑战。
原文摘要 · Abstract (English)
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.
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