Implicit knowledge hidden within the explicit table cells, such as data
insights, is the key to generating a high-quality table summary. However,
unveiling such implicit knowledge is a non-trivial task. Due to the complex
nature of structured tables, it is challenging even for large language models
(LLMs) to mine the implicit knowledge in an insightful and faithful manner. To
address this challenge, we propose a novel table reasoning framework
Question-then-Pinpoint. Our work focuses on building a plug-and-play table
reasoner that can self-question the insightful knowledge and answer it by
faithfully pinpointing evidence on the table to provide explainable guidance
for the summarizer. To train a reliable reasoner, we collect table knowledge by
guiding a teacher LLM to follow the coarse-to-fine reasoning paths and refine
it through two quality enhancement strategies to selectively distill the
high-quality knowledge to the reasoner. Extensive experiments on two table
summarization datasets, including our newly proposed InsTaSumm, validate the
general effectiveness of our framework.

我们提出了一个新颖的表格推理框架 —— 问题 - 指明框架，旨在揭示表格单元中隐含的知识，以生成高质量的表格摘要，并为总结器提供可解释的指导。通过引导教师语言模型按照由粗到细的推理路径进行训练，采用两种质量提升策略，有选择性地提炼高质量的知识给推理器，我们验证了该框架在包括我们新提出的 InsTaSumm 在内的两个表格摘要数据集上的普遍有效性。

利用问题 - 锁定推理器揭示隐式表格知识的洞察性表格摘要

Unveiling Implicit Table Knowledge with Question-Then-Pinpoint Reasoner  for Insightful Table Summarization

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

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。