DeCA: A Decomposition-Enhanced Framework for Query-Focused Table Summarization

Query-focused table summarization aims to generate personalized summaries by reasoning and analyzing tabular data in response to user queries. Existing large language model LLM based methods utilize intermediate facts to enhance reasoning. However, their effectiveness is constrained by limited inference rules and the generation of erroneous sub-queries, potentially introducing misleading information into the summarization. To address these issues, we propose DeCA, an innovative framework designed to generate non-repetitive and relevant sub-queries that support LLM reasoning and improve summary quality. Our framework comprises four modules: 1 Table Schema Extractor that interprets table structure and information 2 Query Decomposer that recursively decomposes queries 3 Sub-query Checker that verifies non-repetition, relevance, and dependencies among sub-queries and 4 Answer Generator that generates summaries employing a hint-based answering strategy. Furthermore, we construct CQTS, the first large-scale Chinese table dataset for query-focused table summarization, consisting of 2,956 tables and 6,721 query-summary pairs. Extensive experiments on CQTS and two English datasets, QTSumm and FeTaQA, demonstrate that DeCA enhances LLM reasoning and outperforms existing methods in summary generation and sub-queries formulation.