People primarily consult tables to conduct data analysis or answer specific
questions. Text generation systems that can provide accurate table summaries
tailored to users' information needs can facilitate more efficient access to
relevant data insights. However, existing table-to-text generation studies
primarily focus on converting tabular data into coherent statements, rather
than addressing information-seeking purposes. In this paper, we define a new
query-focused table summarization task, where text generation models have to
perform human-like reasoning and analysis over the given table to generate a
tailored summary, and we introduce a new benchmark named QTSumm for this task.
QTSumm consists of 5,625 human-annotated query-summary pairs over 2,437 tables
on diverse topics. Moreover, we investigate state-of-the-art models (i.e., text
generation, table-to-text generation, and large language models) on the QTSumm
dataset. Experimental results and manual analysis reveal that our benchmark
presents significant challenges in table-to-text generation for future
research.

本研究定义了一个新的针对查询的表格摘要任务，并提出了一个新的名为 QTSumm 的基准测试，其中包含 5625 个在各种主题的 2437 个表格上人工注释的查询摘要对。实验结果和手动分析表明，我们的基准测试为未来的表格到文本生成研究提供了重要挑战和发展机会。