Theme-Explanation Structure for Table Summarization using Large Language Models: A Case Study on Korean Tabular Data
This addresses the challenge of making tabular data accessible via LLMs for administrative users, though it is incremental as it builds on existing table summarization methods.
The paper tackled the problem of generating human-friendly summaries from complex tables, particularly Korean administrative data, by introducing the Tabular-TX pipeline, which structures outputs into Theme and Explanation parts to enhance readability without requiring fine-tuning.
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.