DataShot: Automatic Generation of Fact Sheets from Tabular Data - Wang, Yun; (intern), Zhida Sun; Zhang, Haidong; Cui, Weiwei; (intern), Ke Xu; Ma, Xiaojuan; Zhang, Dongmei (IEEE Transactions on Visualization and Computer Graphics, 2019)
Fact sheets with vivid graphical design and intriguing statistical insights are prevalent for presenting raw data. They can help audiences understand data-related facts effectively and make a deep impression. However, designing a fact sheet requires both data and design expertise and is a laborious and time-consuming process. One needs to not only understand the data in depth but also produce intricate graphical representations. To assist in the design process, we present DataShot which, to the best of our knowledge, is the first automated system that creates fact sheets automatically from tabular data. First, we conduct a qualitative analysis of 245 infographic examples to explore general infographic design space at both the sheet and element levels. We identify common infographic structures, sheet layouts, fact types, and visualization styles during the study. Based on these findings, we propose a fact sheet generation pipeline, consisting of fact extraction, fact composition, and presentation synthesis, for the auto-generation workflow. To validate our system, we present use cases with three real-world datasets. We conduct an in-lab user study to understand the usage of our system. Our evaluation results show that DataShot can efficiently generate satisfactory fact sheets to support further customization and data presentation