Theme-Explanation Structure for Table Summarization using Large Language Models: A Case Study on Korean Tabular Data
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912472496078848 |
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| author | Kwack, TaeYoon Kim, Jisoo Jung, Ki Yong Lee, DongGeon Park, Heesun |
| author_facet | Kwack, TaeYoon Kim, Jisoo Jung, Ki Yong Lee, DongGeon Park, Heesun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10487 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Theme-Explanation Structure for Table Summarization using Large Language Models: A Case Study on Korean Tabular Data Kwack, TaeYoon Kim, Jisoo Jung, Ki Yong Lee, DongGeon Park, Heesun Computation and Language Artificial Intelligence 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. |
| title | Theme-Explanation Structure for Table Summarization using Large Language Models: A Case Study on Korean Tabular Data |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2501.10487 |