Towards Automated Infographic Authoring From Natural Language Statement With Multiple Proportional Facts

Infographics, which usually contain many well-designed visual elements, have significant advantages in delivering information efficiently and accurately. Previous research shows that proportion-related infographics make up the majority of all infographics. However, the creation of proportion-related...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2024, Vol.26, p.7101-7113
Main Authors: Lu, Jianwei, Wang, Guohua, Cai, Yi, Wu, Xin
Format: Article
Language:English
Subjects:
automated infographic authoring
Costs
Data models
Data visualization
Deep learning
Infographic
natural language processing
Semantics
Surveys
Task analysis
visualization
Citations: Items that this one cites
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Summary:Infographics, which usually contain many well-designed visual elements, have significant advantages in delivering information efficiently and accurately. Previous research shows that proportion-related infographics make up the majority of all infographics. However, the creation of proportion-related infographics is difficult for general users. Recently, many researchers focus on generating infographics from the text with a single proportional fact. Our further research found that users tend to create infographics with multiple proportional facts. Existing research lacks modeling of relations between different facts, resulting in poor performance when generating infographics with multiple facts. In this paper, we model the relationship of different proportional facts based on the results of our investigation and design a deep learning-based model to classify them. At the same time, we also optimize the ability to extract multiple proportional facts from text. The experiments show that our model outperforms existing models when visualizing text with multiple proportional facts.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2024.3360722