Long Text and Multi-Table Summarization: Dataset and Method (2022)

Abstract: 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.

## introduction report documents, like financial reports, investigative reports, and technical reports, are essential information sources. these reports usually contain textual and tabular content. as shown in figure 1 , the salient information can be scattered in long text and multiple tables in each report, which makes it difficult for non-specialized readers to efficiently read and gather salient information from these report documents. automatic document summarization techniques can produce these reports' summaries, which can support readers quickly browsing salient information in these reports. our tar-tables ## method summarizing long text and multiple tables has several challenging issues: identifying the salient information from a large amount of input content, incorporating the text and tabular content into the summary generation, and efficiently processing long input and output sequences. this section presents our solution to the above issues. ## conclusion in this paper, we introduce findsum, the first large-scale dataset for long text and multi-table summarization. built on tens of thousands of annual report documents from thousands of companies, findsum has two subsets for summarizing these companies' results of operations and liquidity. besides, we propose a solution for the long text and multi-table summarization. it has three main steps: data pre-processing, content selection, and summarization. we adopt different content selection methods to select the salient content from the long text and dozens of tables in each report document. as for the summarization step, we present and compare three types of summarization methods incorporating text and tabular data into the summary generation. to assess the usage of numerical information in produced summaries, we propose a set of evaluation metrics. dataset analyses and experimental results indicate the importance of jointly considering input textual and tabular data when generating summaries for report documents.