Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements

Combining data content with visual embellishments, infographics can effectively deliver messages in an engaging and memorable manner. Various authoring tools have been proposed to facilitate the creation of infographics. However, creating a professional infographic with these authoring tools is stil...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2020-01, Vol.26 (1), p.906-916
Main Authors: Cui, Weiwei, Zhang, Xiaoyu, Wang, Yun, Huang, He, Chen, Bei, Fang, Lei, Zhang, Haidong, Lou, Jian-Guan, Zhang, Dongmei
Format: Article
Language:English
Subjects:
and dissemination
Authoring
Authoring systems
automatic visualization
Data visualization
infographic
Natural language
Natural languages
Social networking (online)
Sports
Visualization
Visualization for the masses
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Summary:Combining data content with visual embellishments, infographics can effectively deliver messages in an engaging and memorable manner. Various authoring tools have been proposed to facilitate the creation of infographics. However, creating a professional infographic with these authoring tools is still not an easy task, requiring much time and design expertise. Therefore, these tools are generally not attractive to casual users, who are either unwilling to take time to learn the tools or lacking in proper design expertise to create a professional infographic. In this paper, we explore an alternative approach: to automatically generate infographics from natural language statements. We first conducted a preliminary study to explore the design space of infographics. Based on the preliminary study, we built a proof-of-concept system that automatically converts statements about simple proportion-related statistics to a set of infographics with pre-designed styles. Finally, we demonstrated the usability and usefulness of the system through sample results, exhibits, and expert reviews.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2019.2934785