@inproceedings{tanjila-etal-2025-bengali,
    title = "{B}engali {C}hart{S}umm: A Benchmark Dataset and study on feasibility of Large Language Models on {B}engali Chart to Text Summarization",
    author = "Tanjila, Nahida Akter  and
      Poushi, Afrin Sultana  and
      Farhan, Sazid Abdullah  and
      Kamal, Abu Raihan Mostofa  and
      Hossain, Md. Azam  and
      Ashmafee, Md. Hamjajul",
    editor = "Sarveswaran, Kengatharaiyer  and
      Vaidya, Ashwini  and
      Krishna Bal, Bal  and
      Shams, Sana  and
      Thapa, Surendrabikram",
    booktitle = "Proceedings of the First Workshop on Challenges in Processing South Asian Languages (CHiPSAL 2025)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.chipsal-1.4/",
    pages = "35--45",
    abstract = "In today{'}s data-driven world, effectively organizing and presenting data is challenging, particularly for non-experts. While tabular formats structure data, they often lack intuitive insights; charts, however, prefer accessible and impactful visual summaries. Although recent advancements in NLP, powered by large language models (LLMs), have primarily beneʐʒted high-resource languages like English, low-resource languages such as Bengali{---}spoken by millions globally{---}still face significant data limitations. This research addresses this gap by introducing ``Bengali ChartSumm,'' a benchmark dataset with 4,100 Bengali chart images, metadata, and summaries. This dataset facilitates the analysis of LLMs (mT5, BanglaT5, Gemma) in Bengali chart-to-text summarization, offering essential baselines and evaluations that enhance NLP research for low-resource languages."
}