@inproceedings{ghosh-etal-2025-infogen,
    title = "Infogen: Generating Complex Statistical Infographics from Documents",
    author = "Ghosh, Akash  and
      Garimella, Aparna  and
      Ramu, Pritika  and
      Bandyopadhyay, Sambaran  and
      Saha, Sriparna",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1003/",
    pages = "20552--20570",
    ISBN = "979-8-89176-251-0",
    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 \textit{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, alignment, etc. We also present \textbf{ \textit{Infodat}}, the first benchmark dataset for text-to-infographic metadata generation, where each sample links a document to its metadata. We propose \textbf{ \textit{Infogen}}, a two-stage framework where fine-tuned LLMs first generate metadata, which is then converted into infographic code. Extensive evaluations on \textbf{ \textit{Infodat}} demonstrate that \textbf{ \textit{Infogen}} achieves state-of-the-art performance, outperforming both closed and open-source LLMs in text-to-statistical infographic generation."
}