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.

翻译：人们主要通过查阅表格来进行数据分析或回答具体问题。能够根据用户的信息需求提供准确表格摘要的文本生成系统，将有助于更高效地获取相关数据洞察。受此驱动，我们定义了一项新的查询聚焦表格摘要任务，要求文本生成模型对给定表格进行类似人类的推理与分析，以生成定制化摘要。为此，我们引入了一个名为QTSumm的新基准，其中包含2,934个表格上的7,111个人工标注的查询-摘要对，覆盖多种主题。我们在QTSumm上探究了一系列强基线方法，包括文本生成、表格到文本生成以及大型语言模型。实验结果与人工分析表明，这一新任务为未来表格到文本生成研究带来了显著挑战。此外，我们提出了一种名为ReFactor的新方法，该方法能从表格数据中检索并推理与查询相关的信息，以生成若干自然语言事实。实验结果表明，ReFactor通过将生成的事实拼接至模型输入，能够为基线方法带来改进。我们的数据和代码已公开于https://github.com/yale-nlp/QTSumm。