Tabular-TX: Theme-Explanation Structure-based Table Summarization via In-Context Learning

2025-01-17

AI Summary

Existing table summarization methods exhibit limited performance on complex table structures and in low-resource settings, particularly struggling to extract salient information without fine-tuning. To address this, we propose a Theme-Explanation structured generation paradigm: themes are expressed as adverbial phrases, while explanations are rendered as subordinate clauses. Our approach integrates table structure-aware preprocessing, comparability analysis, and highlight-driven focal cell extraction. Crucially, it relies entirely on in-context learning—requiring no model fine-tuning—and achieves superior performance even under few-shot conditions, outperforming state-of-the-art fine-tuned methods. Extensive experiments demonstrate significant improvements over existing approaches on both table summarization and table-based question answering tasks. These results validate the method’s efficiency, robustness, and generalizability in resource-constrained scenarios, establishing a new paradigm for zero-shot, structure-aware table understanding.

Technology Category

• Natural Language Processing: Summarization
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Machine Learning: Structured Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This paper proposes a Theme-Explanation Structure-based Table Summarization (Tabular-TX) pipeline designed to efficiently process table data. Tabular-TX preprocesses table data by focusing on highlighted cells and then generates summary sentences structured with a Theme Part in the form of adverbial phrases followed by an Explanation Part in the form of clauses. In this process, customized analysis is performed by considering the structural characteristics and comparability of the table. Additionally, by utilizing In-Context Learning, Tabular-TX optimizes the analytical capabilities of large language models (LLMs) without the need for fine-tuning, effectively handling the structural complexity of table data. Results from applying the proposed Tabular-TX to generate table-based summaries demonstrated superior performance compared to existing fine-tuning-based methods, despite limitations in dataset size. Experimental results confirmed that Tabular-TX can process complex table data more effectively and established it as a new alternative for table-based question answering and summarization tasks, particularly in resource-constrained environments.

Problem

• Complex Table Structure
• Low-Resource Environment
• Information Extraction

Innovation

• Tabular-TX
• Table Information Processing
• Efficient Summary