Infogen: Generating Complex Statistical Infographics from Documents

• URL: http://arxiv.org/abs/2507.20046v1
• Date: Sat, 26 Jul 2025 19:38:46 GMT
• Title: Infogen: Generating Complex Statistical Infographics from Documents
• Authors: Akash Ghosh, Aparna Garimella, Pritika Ramu, Sambaran Bandyopadhyay, Sriparna Saha,
• Abstract summary: We introduce the task of generating infographics composed of multiple sub-charts (e.g., line, bar, pie) that are contextually accurate, insightful, and visually aligned.<n>To achieve this, we define infographic metadata that includes its title and textual insights, along with sub-chart-specific details such as their corresponding data and alignment.<n>We also present Infodat, the first benchmark dataset for text-to-infographic metadata generation.
• Score: 29.46917658452633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Statistical infographics are powerful tools that simplify complex data into visually engaging and easy-to-understand formats. Despite advancements in AI, particularly with LLMs, existing efforts have been limited to generating simple charts, with no prior work addressing the creation of complex infographics from text-heavy documents that demand a deep understanding of the content. We address this gap by introducing the task of generating statistical infographics composed of multiple sub-charts (e.g., line, bar, pie) that are contextually accurate, insightful, and visually aligned. To achieve this, we define infographic metadata that includes its title and textual insights, along with sub-chart-specific details such as their corresponding data and alignment. We also present Infodat, the first benchmark dataset for text-to-infographic metadata generation, where each sample links a document to its metadata. We propose Infogen, a two-stage framework where fine-tuned LLMs first generate metadata, which is then converted into infographic code. Extensive evaluations on Infodat demonstrate that Infogen achieves state-of-the-art performance, outperforming both closed and open-source LLMs in text-to-statistical infographic generation.

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