Theme-Explanation Structure for Table Summarization using Large Language Models: A Case Study on Korean Tabular Data

This paper proposes the Theme-Explanation Structure-based Table Summarization (Tabular-TX) pipeline designed to process tabular data efficiently. Tabular-TX preprocesses tabular data by focusing on highlighted cells. It then generates summary sentences following a structured format, where the Theme Part appears as an adverbial phrase, and the Explanation Part follows as a predictive clause. This approach enables tailored analysis by considering the structural characteristics of tables and their comparability. Unlike conventional fine-tuning approaches that require extensive labeled data and computational resources, our method leverages In-Context Learning to dynamically adapt to different table structures without additional training, ensuring efficient and scalable table interpretation. Experimental results demonstrate that Tabular-TX significantly outperforms conventional fine-tuning-based methods, particularly in low-resource scenarios, by leveraging table structures and metadata more effectively through structured prompts. The results confirm that Tabular-TX enables more effective processing of complex tabular data. Furthermore, it serves as a viable alternative for table-based question answering and summarization tasks in resource-constrained environments.