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Visual data storytelling is gaining importance as a means of presentingdata-driven information or analysis results, especially to the general public.This has resulted in design principles being proposed for data-drivenstorytelling, and new authoring tools being created to aid such storytelling.However, data analysts typically lack sufficient background in design andstorytelling to make effective use of these principles and authoring tools. Toassist this process, we present ChartStory for crafting data stories from acollection of user-created charts, using a style akin to comic panels to implythe underlying sequence and logic of data-driven narratives. Our approach is tooperationalize established design principles into an advanced pipeline whichcharacterizes charts by their properties and similarity, and recommends ways topartition, layout, and caption story pieces to serve a narrative. ChartStoryalso augments this pipeline with intuitive user interactions for visualrefinement of generated data comics. We extensively and holistically evaluateChartStory via a trio of studies. We first assess how the tool supports datacomic creation in comparison to a manual baseline tool. Data comics from thisstudy are subsequently compared and evaluated to ChartStory's automatedrecommendations by a team of narrative visualization practitioners. This isfollowed by a pair of interview studies with data scientists using their owndatasets and charts who provide an additional assessment of the system. We findthat ChartStory provides cogent recommendations for narrative generation,resulting in data comics that compare favorably to manually-created ones.

ChartStory: Automated Partitioning, Layout, and Captioning of Charts into Comic-Style Narratives