@inproceedings{aggarwal-etal-2025-goal,
    title = "Goal-Driven Data Story, Narrations and Explanations",
    author = "Aggarwal, Aniya  and
      Gupta, Ankush  and
      Bithel, Shivangi  and
      Agarwal, Arvind",
    editor = "Chen, Weizhu  and
      Yang, Yi  and
      Kachuee, Mohammad  and
      Fu, Xue-Yong",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-industry.56/",
    pages = "684--694",
    ISBN = "979-8-89176-194-0",
    abstract = "In this paper, we propose a system designed to process and interpret vague, open-ended, and multi-line complex natural language queries, transforming them into coherent, actionable data stories. Our system{'}s modular architecture comprises five components{---}Question Generation, Answer Generation, NLG/Chart Generation, Chart2Text, and Story Representation{---}each utilizing LLMs to transform data into human-readable narratives and visualizations. Unlike existing tools, our system uniquely addresses the ambiguity of vague, multi-line queries, setting a new benchmark in data storytelling by tackling complexities no existing system comprehensively handles. Our system is cost-effective, which uses open-source models without extra training and emphasizes transparency by showcasing end-to-end processing and intermediate outputs. This enhances explainability, builds user trust, and clarifies the data story generation process."
}