基于大语言模型的表格摘要主题-解释结构：以韩语表格数据为例的案例研究
Theme-Explanation Structure for Table Summarization using Large Language Models: A Case Study on Korean Tabular Data
AI总结：
本文提出Tabular-TX流水线，通过多步推理与记者角色提示生成主题-解释结构的表格摘要，无需微调即可高效处理韩语行政表格，提升可读性并适用于低资源场景。
摘要：
表格是行政领域中传达关键信息的主要媒介，然而其复杂性阻碍了大语言模型（LLMs）对其的利用。本文介绍了基于主题-解释结构的表格摘要（Tabular-TX）流水线，这是一种旨在从表格数据生成高度可解释摘要的新方法，特别关注韩语行政文档。当前的表格摘要方法往往忽视了人类友好输出这一关键方面。Tabular-TX通过首先采用多步推理过程确保LLMs对表格的深入理解，随后采用记者角色提示策略生成清晰句子来解决这一问题。至关重要的是，它随后将输出结构化为主题部分（状语短语）和解释部分（谓语从句），显著提高了可读性。我们的方法利用上下文学习，无需大量微调及相关的标注数据或计算资源。实验结果表明，Tabular-TX能有效处理复杂的表格结构和元数据，为生成以人为中心的表格摘要提供了一种稳健且高效的解决方案，尤其是在低资源场景下。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
