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Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements Weiwei Cui, Xiaoyu Zhang, Yun Wang, He Huang, Bei Chen, Lei Fang, Haidong Zhang, Jian-Guan Lou, and Dongmei Zhang More than 20% of smartphone users are social network users OF SMARTPHONE USERS More than 20% of More than MORE THAN ARE SOCIAL NETWORK smartphone users are USERS of smartphone users 20% social network users 20% are social network users (a) (b) (c) (d) 3 IN 5 65% 40% Chinese people live in rural areas 65% 3 in 5 40% Chinese people USA freshwater live in rural areas of coffee is consumed at of coffee is of USA freshwater is for agriculture is for agriculture breakfast consumed at breakfast (e) (f) (g) (h) (i) (j) ONLINE TRAFFIC AMONG ALL STUDENTS Among all students AMONG ALL STUDENTS Online Traffic Like football 51.5% Humans Like football 49% Like football Like basketball Like baseball 51.5% Humans 19.5% Good bots Like basketball 33% Like basketball 19.5% Good bots 49% 33% 21% 29% Bad bots Like baseball 21% Like baseball 29% Bad bots (k) (l) (m) (n) (o) Figure 1. Examples created by Text-to-Viz. (a)-(d) are generated from the statement: “More than 20% of smartphone users are social network users.” (e) and (f) are generated from the statement: “40 percent of USA freshwater is for agriculture.” (g) and (h) are generated from the statement: “3 in 5 Chinese people live in rural areas.” (i) and (j) are generated from the statement: “65% of coffee is consumed at breakfast.” (k)-(m) are generated from the statement: “Among all students, 49% like football, 32% like basketball, and 21% like baseball.” (n) and (o) are generated from the statement: “Humans made 51.5% of online traffic, while good bots made 19.5% and bad bots made 29%.” Abstract— 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. Index Terms—Visualization for the masses, infographic, automatic visualization, presentation, and dissemination. 1 INTRODUCTION tages, they are widely used in many areas, such as business, finance, and health-care, for advertisements and communications. However, Information graphics (a.k.a. infographics) is a type of data visualiza- creating a professional infographic is not an easy task. It is a time- tion that combines artistic elements (e.g. clip arts and images) with consuming process and also often requires designer skills to ensure data-driven content (e.g. bar graphs and pie charts) to deliver informa- the perceptual effectiveness and aesthetics. tion in an engaging and memorable manner [24].Due to these advan- Much research has been devoted to investigating the design aspect of infographics [12, 27, 63] and developing authoring tools [30, 58, 69] • W. Cui, Y. Wang, H. Huang, B. Chen, L. Fang, H. Zhang, J. Lou, and D. to facilitate the creation of data-driven infographics. Based on differ- Zhang are with Microsoft Research Asia. Emails: fweiweicu, wangyun, ent considerations, these authoring tools all strive to reach a balance rayhuang, beichen, leifa, haizhang, jlou, and [email protected]. between the ease-of-use and the power of features, and then to speed • X. Zhang is with the ViDi Research Group in University of California, up the authoring process. However, these tools generally target ad- Davis, and this work was done during an internship at Microsoft Research vanced users. With complicated editing operations and technical con- Asia. Email: [email protected]. cepts, these tools are not friendly to casual users, who, we believe, form another major category of infographic creators, other than pro- Manuscript received xx xxx. 201x; accepted xx xxx. 201x. Date of Publication fessionals, such as graphic designers and data scientists [36]. xx xxx. 201x; date of current version xx xxx. 201x. For information on obtaining reprints of this article, please send e-mail to: [email protected]. Consider a hypothetical example in which a program manager, Digital Object Identifier: xx.xxxx/TVCG.201x.xxxxxxx Nina, is preparing a presentation for her manager and wants to em- phasize in her slides that “40% of US kids like video games.” She decides to add an infographic next to the statement with an authoring annotations [53], and imageries [7, 16], do not seem to interfere with tool, e.g., DDG [30] or InfoNice [69]. Since Nina is not a professional the correct perception of information, and can increase the long-term graphic designer, she first needs to spend time (re-)familiarizing her- memorability of visualizations. For example, Haroz et al. [23] demon- self with the tool, such as the user interface and work flow. However, strated that embellished charts are equivalent to plain charts in terms of even if she were familiar with the tool, she still may not know where to reading speed and accuracy, but the added visual elements make them begin to create a desired infographic, because all the existing authoring more memorable. Recently, studies have been conducted to directly tools assume that the users have a clear or rough idea of what the final understand infographics. For example, Bylinskii et al. [15] adopted infographic may look like. Unfortunately, Nina has no design exper- OCR techniques to assign hashtags to infographics for information re- tise and has little to no knowledge of how a professional infographic trieval purposes. Madan et al. [39] used computer vision techniques to would look like. Therefore, she likely needs to go through existing detect icons in infographics and proposed a solution to automatically well-designed infographics (in books or on the Internet) to look for in- summarize infographics. spiration. Based on the examined samples, she then settles on a design Although these studies have extensively demonstrated the value of choice that has the best “return of investment” in terms of authoring infographics from various perspectives, and started to investigate info- time and her purpose of emphasizing the message. graphic understanding in general, none of them shares the same goal From this example, we can summarize some common patterns for of ours, which is to build data-driven infographics automatically for this user category. First, creators in this category only occasionally general users. create infographics and thus are not proficient in the authoring tools. Second, they do not aim for the most creative/impressive infographics, 2.2 Natural Language Interactions which often involve complex designs and a long authoring time and are unnecessary in terms of “return of investment”. Instead, something Research on natural language processing [71] provides effective ways effective but professional is often sufficient for their purposes. Third, to analyze texts. Syntax analysis [20] and semantic analysis [8] can they often only have little design expertise, and would likely be unclear be used to discover hierarchical structures and understand meanings on how to design a decent infographic from scratch. On the other hand, in text, but they are more appropriate for general language under- if they were provided with good and relevant samples, they could often standing problems. Sequence tagging tasks, such as Part-Of-Speech quickly pick one based on their personal preferences. tagging [40] and Named Entity Recognition (NER) [48], aim to as- To address the needs of users in this category, we explored a new sign a categorical label to each word in text sequences, after that, approach: to automatically generate infographics based on text state- text segments of different types can be obtained based on assigned ments. Since text is the most common form of communication to ex- categorical labels. Since the input of our system requires extracting change information, we believe that this approach can help more peo- text segments, we adopt the sequence tagging techniques. For se- ple take advantage of infographics. To achieve this goal, there are two quence tagging techniques, rule-based methods [52] are straightfor- major challenges to overcome. The first one is to understand and ex- ward, but costly and limited. There are also many machine learning tract appropriate information from a given statement. The second one methods proposed which are more robust and easier to generalize, in- is to construct professional infographics based on the extracted infor- cluding Hidden Markov Models (HMM) [9], Support Vector Machines mation. For the text understanding challenge, we first collected a cor- (SVM) [4] and Conditional Random Fields (CRF) [34]. pus of real-world samples. Then, these samples were manually labeled Recently, human languages have been used as an interface for vi- to train a CRF-based model to identify and extract information for in- sual data analysis. Systems like Articulate [66], DataTone [22], and fographic constructions. For the infographic construction challenge, Eviza [60] adopt various methods to transpile natural language queries we analyzed and explored the design space of infographic exemplars to formal database queries, such as SQL, and extract analysis results that we collected from the Internet.

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