@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/",
doi = "10.18653/v1/2025.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."
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%0 Conference Proceedings
%T Goal-Driven Data Story, Narrations and Explanations
%A Aggarwal, Aniya
%A Gupta, Ankush
%A Bithel, Shivangi
%A Agarwal, Arvind
%Y Chen, Weizhu
%Y Yang, Yi
%Y Kachuee, Mohammad
%Y Fu, Xue-Yong
%S 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)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-194-0
%F aggarwal-etal-2025-goal
%X 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.
%R 10.18653/v1/2025.naacl-industry.56
%U https://aclanthology.org/2025.naacl-industry.56/
%U https://doi.org/10.18653/v1/2025.naacl-industry.56
%P 684-694
Markdown (Informal)
[Goal-Driven Data Story, Narrations and Explanations](https://aclanthology.org/2025.naacl-industry.56/) (Aggarwal et al., NAACL 2025)
ACL
- Aniya Aggarwal, Ankush Gupta, Shivangi Bithel, and Arvind Agarwal. 2025. Goal-Driven Data Story, Narrations and Explanations. In 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), pages 684–694, Albuquerque, New Mexico. Association for Computational Linguistics.