@inproceedings{yang-etal-2025-kahan,
    title = "{KAHAN}: Knowledge-Augmented Hierarchical Analysis and Narration for Financial Data Narration",
    author = "Yang, Yajing  and
      Deng, Tony  and
      Kan, Min-Yen",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.findings-emnlp.1405/",
    doi = "10.18653/v1/2025.findings-emnlp.1405",
    pages = "25761--25785",
    ISBN = "979-8-89176-335-7",
    abstract = "We propose KAHAN, a knowledge-augmented hierarchical framework that systematically extracts insights from raw tabular data at entity, pairwise, group, and system levels. KAHAN uniquely leverages LLMs as domain experts to drive the analysis. On DataTales financial reporting benchmark, KAHAN outperforms existing approaches by over 20{\%} on narrative quality (GPT-4o), maintains 98.2{\%} factuality, and demonstrates practical utility in human evaluation. Our results reveal that knowledge quality drives model performance through distillation, hierarchical analysis benefits vary with market complexity, and the framework transfers effectively to healthcare domains. The data and code are available at https://github.com/yajingyang/kahan."
}