Goal-Driven Data Story, Narrations and Explanations
Aniya Aggarwal, Ankush Gupta, Shivangi Bithel, Arvind Agarwal
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.
BibTeX
@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://aclanthology.org/2025.naacl-industry.56/",
pages = "684--694",
ISBN = "979-8-89176-194-0"
}