Tabular-TX: Theme-Explanation Structure-based Table Summarization via In-Context Learning
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.
Code (0)
등록된 구현이 없습니다.
Tasks
In-Context LearningQuestion AnsweringTable-based Question AnsweringSimilar Papers 제목 키워드 기반
DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization
Query-focused tabular summarization is an emerging task in table-to-text generation that synthesizes a summary response from tabular data based on user queries. Traditional transformer-based approaches face challenges du…
DecoderLanguage ModelingLanguage ModellingLarge Language Model+3CAST: Achieving Stable LLM-based Text Analysis for Data Analytics
Text analysis of tabular data relies on two core operations: \emph{summarization} for corpus-level theme extraction and \emph{tagging} for row-level labeling. A critical limitation of employing large language models (LLM…
Moneyball with LLMs: Analyzing Tabular Summarization in Sports Narratives
Large language model (LLM) approaches to tabular summarization rely on extensive prompt engineering, decomposition pipelines, or entity-level intermediate representations to achieve strong performance. While effective, t…
Prompt EngineeringREDTABS: A Collection of Report Document Datasets for Long Text and Multi-Table Summarization
Automatic document summarization aims to produce a concise summary covering the input document's salient content. Within a report document, both the textual and non-textual content (e.g., tables and figures) can be impo…
Document SummarizationInformativenessText GenerationHarnessing LLMs Explanations to Boost Surrogate Models in Tabular Data Classification
Large Language Models (LLMs) have shown remarkable ability in solving complex tasks, making them a promising tool for enhancing tabular learning. However, existing LLM-based methods suffer from high resource requirements…
Explanation GenerationIn-Context Learning