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.

翻译：在小说和新闻等叙事文本数量呈指数级增长的背景下，由于人类工作记忆和注意力持续时间的限制，读者难以从这些复杂的文本中提取并持续记忆故事线。为解决此问题，我们提出了一种可视化方法StoryExplorer，它促进了叙事文本知识外化的过程，并进一步使心智模型的形式更加连贯。通过形成性研究以及与2位领域专家的密切合作，我们识别了提取故事线的关键挑战。在提炼出的需求指导下，我们随后提出了一套工作流程（即洞察发现-脚本编写-故事讲述），使用户能够交互式地生成叙事结构片段。接着，我们提出了一个可视化系统StoryExplorer，它结合了笔划标注和基于GPT的视觉提示，以快速提取故事片段并交互式地构建故事线。为评估StoryExplorer的有效性和实用性，我们进行了2个案例研究，并对16位目标用户进行了深度访谈。结果表明，用户通过使用StoryExplorer及所提出的工作流程，能够更好地提取故事线。