StoryExplorer : A Visualization Framework for Storyline Generation of Textual Narratives

In the context of the exponentially increasing volume of narrative texts such as novels and news, readers struggle to extract and consistently remember storylines from these intricate texts due to the constraints of human working memory and attention span. To tackle this issue, we propose a visualization approachStoryExplorer, 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 two 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 systemStoryExplorerthat combines stroke annotation and GPT-based visual hints to quickly extract story fragments and interactively construct storylines. To evaluate the effectiveness and usefulness ofStoryExplorer, we conducted two case studies and in-depth user interviews with 16 target users. The result shows that users can conveniently and effectively extract the storyline by usingStoryExploreralong with the proposed workflow.