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.

我们提出了一种新方法来满足用户对表格总结的信息和质量要求，并且利用文本查询和多个表格生成与用户信息需求相适应的查询依赖的表格总结。我们通过使用特定于该任务的综合数据集展开了广泛的实验证明了我们提出的方法相对于基准方法的有效性，并为查询依赖的多表总结的研究进展提供了洞察。

QFMTS: 生成多表输入的查询关注摘要

QFMTS: Generating Query-Focused Summaries over Multi-Table Inputs

In many settings, interventions may be more effective for some individuals
than others, so that targeting interventions may be beneficial. We analyze the
value of targeting in the context of a large-scale field experiment with over
53,000 college students, where the goal was to use "nudges" to encourage
students to renew their financial-aid applications before a non-binding
deadline. We begin with baseline approaches to targeting. First, we target
based on a causal forest that estimates heterogeneous treatment effects and
then assigns students to treatment according to those estimated to have the
highest treatment effects. Next, we evaluate two alternative targeting
policies, one targeting students with low predicted probability of renewing
financial aid in the absence of the treatment, the other targeting those with
high probability. The predicted baseline outcome is not the ideal criterion for
targeting, nor is it a priori clear whether to prioritize low, high, or
intermediate predicted probability. Nonetheless, targeting on low baseline
outcomes is common in practice, for example because the relationship between
individual characteristics and treatment effects is often difficult or
impossible to estimate with historical data. We propose hybrid approaches that
incorporate the strengths of both predictive approaches (accurate estimation)
and causal approaches (correct criterion); we show that targeting intermediate
baseline outcomes is most effective, while targeting based on low baseline
outcomes is detrimental. In one year of the experiment, nudging all students
improved early filing by an average of 6.4 percentage points over a baseline
average of 37% filing, and we estimate that targeting half of the students
using our preferred policy attains around 75% of this benefit.

通过使用‘鼓励’（nudges）来推动大学生在截止日期前更新财政援助申请，当研究者通过异质性治疗效应估计再将学生指派给相应的治疗组时，下一步通过衡量低预测概率及高预测概率的学生对于应用的回归效果，从而验证多种指标中通过靶向中间预线性结果是最有效的方法。