  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.
