Automatic document summarization aims to produce a concise summary covering
the input document's salient information. Within a report document, the salient
information can be scattered in the textual and non-textual content. However,
existing document summarization datasets and methods usually focus on the text
and filter out the non-textual content. Missing tabular data can limit produced
summaries' informativeness, especially when summaries require covering
quantitative descriptions of critical metrics in tables. Existing datasets and
methods cannot meet the requirements of summarizing long text and multiple
tables in each report. To deal with the scarcity of available data, we propose
FINDSum, the first large-scale dataset for long text and multi-table
summarization. Built on 21,125 annual reports from 3,794 companies, it has two
subsets for summarizing each company's results of operations and liquidity. To
summarize the long text and dozens of tables in each report, we present three
types of summarization methods. Besides, we propose a set of evaluation metrics
to assess the usage of numerical information in produced summaries. Dataset
analyses and experimental results indicate the importance of jointly
considering input textual and tabular data when summarizing report documents.

旨在创造简明摘要以涵盖输入文档的要点信息。自动文档摘要对文本和非文本内容进行了讨论，提出了涵盖定量描述的关键指标的表格数据的重要性，提出了一个大规模数据集和三种摘要方法，还评估了评估指标

长文本和多表总结：数据集和方法

Long Text and Multi-Table Summarization: Dataset and Method

We propose a method to perform automatic document summarisation without using
reference summaries. Instead, our method interactively learns from users'
preferences. The merit of preference-based interactive summarisation is that
preferences are easier for users to provide than reference summaries. Existing
preference-based interactive learning methods suffer from high sample
complexity, i.e. they need to interact with the oracle for many rounds in order
to converge. In this work, we propose a new objective function, which enables
us to leverage active learning, preference learning and reinforcement learning
techniques in order to reduce the sample complexity. Both simulation and
real-user experiments suggest that our method significantly advances the state
of the art. Our source code is freely available at
this https URL

该研究提出了一种基于用户偏好进行交互式自动摘要的方法，通过利用主动学习、偏好学习和强化学习等技术，提高了方法的样本复杂性，并在模拟和真实用户实验中得到了显著的结果提升。