Theme-Explanation Structure for Table Summarization using Large Language Models: A Case Study on Korean Tabular Data
TaeYoon Kwack, Jisoo Kim, Ki Yong Jung, DongGeon Lee, Heesun Park
Abstract
Tables are a primary medium for conveying critical information in administrative domains, yet their complexity hinders utilization by Large Language Models (LLMs). This paper introduces the Theme-Explanation Structure-based Table Summarization (Tabular-TX) pipeline, a novel approach designed to generate highly interpretable summaries from tabular data, with a specific focus on Korean administrative documents. Current table summarization methods often neglect the crucial aspect of human-friendly output. Tabular-TX addresses this by first employing a multi-step reasoning process to ensure deep table comprehension by LLMs, followed by a journalist persona prompting strategy for clear sentence generation. Crucially, it then structures the output into a Theme Part (an adverbial phrase) and an Explanation Part (a predicative clause), significantly enhancing readability. Our approach leverages in-context learning, obviating the need for extensive fine-tuning and associated labeled data or computational resources. Experimental results show that Tabular-TX effectively processes complex table structures and metadata, offering a robust and efficient solution for generating human-centric table summaries, especially in low-resource scenarios.- Anthology ID:
- 2025.trl-workshop.1
- Volume:
- Proceedings of the 4th Table Representation Learning Workshop
- Month:
- July
- Year:
- 2025
- Address:
- Vienna, Austria
- Editors:
- Shuaichen Chang, Madelon Hulsebos, Qian Liu, Wenhu Chen, Huan Sun
- Venues:
- TRL | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–12
- Language:
- URL:
- https://preview.aclanthology.org/acl25-workshop-ingestion/2025.trl-workshop.1/
- DOI:
- Cite (ACL):
- TaeYoon Kwack, Jisoo Kim, Ki Yong Jung, DongGeon Lee, and Heesun Park. 2025. Theme-Explanation Structure for Table Summarization using Large Language Models: A Case Study on Korean Tabular Data. In Proceedings of the 4th Table Representation Learning Workshop, pages 1–12, Vienna, Austria. Association for Computational Linguistics.
- Cite (Informal):
- Theme-Explanation Structure for Table Summarization using Large Language Models: A Case Study on Korean Tabular Data (Kwack et al., TRL 2025)
- PDF:
- https://preview.aclanthology.org/acl25-workshop-ingestion/2025.trl-workshop.1.pdf