Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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
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.

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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: math
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga