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

• URL: http://arxiv.org/abs/2501.10487v2
• Date: Wed, 26 Feb 2025 07:10:17 GMT
• Title: Theme-Explanation Structure for Table Summarization using Large Language Models: A Case Study on Korean Tabular Data
• Authors: TaeYoon Kwack, Jisoo Kim, Ki Yong Jung, DongGeon Lee, Heesun Park,
• Abstract summary: This paper proposes the Theme-Explanation Structure-based Table Summarization pipeline (Tabular-TX)<n>It 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.<n> Experimental results demonstrate that Tabular-TX significantly outperforms conventional fine-tuning-based methods.
• Score: 1.0621665950143144
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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