StoryExplorer: A Visualization Framework for Storyline Generation of Textual Narratives

TL;DR

StoryExplorer is a visualization framework that helps users extract and understand storylines from complex textual narratives through interactive visualization and GPT-based hints, improving comprehension and memory retention.

Contribution

The paper introduces a novel visualization system combining stroke annotation and GPT hints, along with a workflow for interactive storyline generation from texts.

Findings

Users better extract storylines with StoryExplorer

System facilitates knowledge externalization of narratives

Effective in case studies and user interviews

Abstract

In the context of the exponentially increasing volume of narrative texts such as novels and news, readers struggle to extract and consistently remember storyline from these intricate texts due to the constraints of human working memory and attention span. To tackle this issue, we propose a visualization approach StoryExplorer, which facilitates the process of knowledge externalization of narrative texts and further makes the form of mental models more coherent. Through the formative study and close collaboration with 2 domain experts, we identified key challenges for the extraction of the storyline. Guided by the distilled requirements, we then propose a set of workflow (i.e., insight finding-scripting-storytelling) to enable users to interactively generate fragments of narrative structures. We then propose a visualization system StoryExplorer which combines stroke annotation and GPT-based visual hints to quickly extract story fragments and interactively construct storyline. To evaluate the effectiveness and usefulness of StoryExplorer, we conducted 2 case studies and in-depth user interviews with 16 target users. The result shows that users can better extract the storyline by using StoryExplorer along with the proposed workflow.