  Table summarization is a crucial task aimed at condensing information from
tabular data into concise and comprehensible textual summaries. However,
existing approaches often fall short of adequately meeting users' information
and quality requirements and tend to overlook the complexities of real-world
queries. In this paper, we propose a novel method to address these limitations
by introducing query-focused multi-table summarization. Our approach, which
comprises a table serialization module, a summarization controller, and a large
language model (LLM), utilizes textual queries and multiple tables to generate
query-dependent table summaries tailored to users' information needs. To
facilitate research in this area, we present a comprehensive dataset
specifically tailored for this task, consisting of 4909 query-summary pairs,
each associated with multiple tables. Through extensive experiments using our
curated dataset, we demonstrate the effectiveness of our proposed method
compared to baseline approaches. Our findings offer insights into the
challenges of complex table reasoning for precise summarization, contributing
to the advancement of research in query-focused multi-table summarization.
