  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. Motivated by this, 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. We
introduce a new benchmark named QTSumm for this task, which contains 7,111
human-annotated query-summary pairs over 2,934 tables covering diverse topics.
We investigate a set of strong baselines on QTSumm, including text generation,
table-to-text generation, and large language models. Experimental results and
manual analysis reveal that the new task presents significant challenges in
table-to-text generation for future research. Moreover, we propose a new
approach named ReFactor, to retrieve and reason over query-relevant information
from tabular data to generate several natural language facts. Experimental
results demonstrate that ReFactor can bring improvements to baselines by
concatenating the generated facts to the model input. Our data and code are
publicly available at https://github.com/yale-nlp/QTSumm.
