Graphical overlays that layer visual elements onto charts, are effective to convey insights and context in financial narrative visualizations. However, automating graphical overlays is challenging due to complex narrative structures and limited understanding of effective overlays. To address the challenge, we first summarize the commonly used graphical overlays and narrative structures, and the proper correspondence between them in financial narrative visualizations, elected by a survey of 1752 layered charts with corresponding narratives. We then design FinFlier, a two-stage innovative system leveraging a knowledge-grounding large language model to automate graphical overlays for financial visualizations. The text-data binding module enhances the connection between financial vocabulary and tabular data through advanced prompt engineering, and the graphics overlaying module generates effective overlays with narrative sequencing. We demonstrate the feasibility and expressiveness of FinFlier through a gallery of graphical overlays covering diverse financial narrative visualizations. Performance evaluations and user studies further confirm system's effectiveness and the quality of generated layered charts.

翻译：在金融叙事可视化中，将视觉元素叠加到图表上的图形叠加层能有效传达见解与背景信息。然而，由于叙事结构复杂且对有效叠加层的理解有限，实现图形叠加自动化颇具挑战。为应对此挑战，我们首先通过对1752个带有对应叙事的层叠图表进行调研，总结了金融叙事可视化中常用的图形叠加层、叙事结构及其间的对应关系。随后，我们设计了FinFlier——一个利用知识增强大语言模型实现金融可视化图形叠加自动化的两阶段创新系统。其文本-数据绑定模块通过先进的提示工程增强金融术语与表格数据间的关联，图形叠加模块则通过叙事序列生成有效的叠加层。我们通过涵盖多样化金融叙事可视化的图形叠加案例库，展示了FinFlier的可行性与表现力。性能评估与用户研究进一步证实了系统的有效性及生成层叠图表的品质。