Theme-Explanation Structure for Table Summarization using Large Language Models: A Case Study on K orean Tabular Data (2025)

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.

## introduction tables are essential for presenting core information, especially within the administrative domain, where critical data is frequently structured in tabular formats @xcite . the ability of large language models (llms) to accurately summarize and elucidate the contents of these tables is becoming increasingly significant for data utilization. an important aspect of effective table summarization is the generation of human-understandable output. this necessitates not only the llm's profound comprehension of the input table, but also the crafting of summaries that are both intuitive and concise, delivering key information without ambiguity. despite the critical need for human-centric summaries, recent research in table-to-text generation has often overlooked this aspect. many existing approaches prioritize other metrics or model architectures, without sufficiently addressing how the generated text will be perceived and understood @xcite . consequently, while the output can be factually correct, they lack the clarity, conciseness, or intuitive structure that facilitates effortless human comprehension, particularly when dealing with specialized data such as korean administrative tables @xcite . to address this gap and emphasize the generation of human-friendly summaries, we propose the theme-explanation structure-based table sum marization (tabular-tx) pipeline. our approach is meticulously designed to guide llms towards producing summaries that are not only accurate but also exceptionally interpretable. first, to ensure a deep understanding of the input table, we decompose the llm's reasoning process into a multi-step procedure, where each step focuses on a specific inferential task, thereby simplifying the complex table interpretation process. second, we employ a "journalist persona" prompting strategy to encourage the generation of clear, objective, and well-phrased sentences. finally, and most critically, we transform these generated insights into a highly structured theme-explanation (tx) format, where each summary segment consists of a thematic adverbial phrase followed by a predicative explanatory clause, enhancing readability and directness. the tabular-tx pipeline offers significant advantages, particularly in resource-constrained environments where extensive fine-tuning is unfeasible. our primary contributions are threefold: ## theme-explanation structure unlike conventional approaches that treat table summaries as isolated text generation tasks, our method ensures structural consistency by explicitly organizing content into a theme part and an explanation part. ## tabular-tx pipeline generating summaries with the theme-explanation structure in tabular-tx involves multiple processing steps to transform tabular data into structured natural language summaries. foot_1 ## conclusion this study introduced the theme-explanation table summarization (tabular-tx) pipeline, a novel approach to improve table summarization tasks with low-resource requirements. moreover, the proposed pipeline effectively overcame unique challenges in korean administrative table processing. experimental results signify that tabular-tx enhances table summarization performance. this study contributed to the summarization of complex table data by introducing a novel sentence generation method based on the theme-explanation structure. furthermore, tabular-tx achieved excellent performance without fine-tuning, by incorporating icl. this indicates its potential as a significant contribution to table data analysis, even in resourceconstrained environments, without requiring direct model training.