Results of the summarizing process for the arXiv paper: 2302.03815v1

In the field of natural language processing, automatic document summarization is a crucial task that aims to produce a concise summary covering the input document's salient information. However, existing document summarization datasets and methods usually focus on the text and filter out non-textual content, which can limit produced summaries' informativeness. In particular, missing tabular data can be a significant issue when summaries require covering quantitative descriptions of critical metrics in tables. To address these limitations, Shuaiqi Liu et al. propose FINDSum - the first large-scale dataset for long text and multi-table summarization. This dataset is built on 21,125 annual reports from 3,794 companies and 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 effectively, they present three types of summarization methods: extractive-based methods that select important sentences from the input text; abstractive-based methods that generate new sentences based on the input; and hybrid-based methods that combine both extractive and abstractive approaches. Furthermore, to assess the usage of numerical information in produced summaries accurately, they propose a set of evaluation metrics. Dataset analyses and experimental results indicate that jointly considering input textual and tabular data is crucial when summarizing report documents. Overall, this work provides an essential contribution to advancing automatic document summarization by addressing some significant limitations in current approaches while also providing a new benchmark dataset for future research efforts.

• Automatic document summarization is important in natural language processing.
• Existing methods and datasets focus on text and filter out non-textual content, limiting informativeness.
• Missing tabular data can be a significant issue for quantitative descriptions of critical metrics in tables.
• Shuaiqi Liu et al. propose FINDSum - the first large-scale dataset for long text and multi-table summarization.
• The dataset is built on 21,125 annual reports from 3,794 companies and has two subsets for summarizing each company's results of operations and liquidity.
• Three types of summarization methods are presented: extractive-based, abstractive-based, and hybrid-based.
• A set of evaluation metrics is proposed to assess the usage of numerical information in produced summaries accurately.
• Jointly considering input textual and tabular data is crucial when summarizing report documents.
• This work provides an essential contribution to advancing automatic document summarization by addressing some significant limitations in current approaches while also providing a new benchmark dataset for future research efforts.

Automatic document summarization is when a computer program makes a short summary of a long text. Some methods only work on words and not tables, which can be a problem for important numbers. A group of people made a big dataset to help with summarizing long texts and tables together. They made two parts of the dataset, one for talking about how well a company did and another for talking about how much money they have. There are three ways to make summaries: taking out important sentences, making new sentences that mean the same thing, or using both ways together. The group also made some rules to check if the summary has all the right numbers in it. It's important to look at both words and tables when making summaries like this. This new way of summarizing is really helpful because it fixes some problems that old ways had and gives us more information to use in the future! Definitions- Automatic document summarization: When a computer program makes a short summary of a long text. - Dataset: A collection of data used for research or analysis. - Extractive-based summarization: Taking out important sentences from the original text to make a summary. - Abstractive-based summarization: Making new sentences that mean the same thing as the original text to make a summary. - Hybrid-based summarization: Using both extractive-based and abstractive-based methods together to make a summary. - Evaluation metrics: Rules used to check if something is good enough or accurate enough. - Tabular data

Introducing FINDSum: A New Dataset for Long Text and Multi-Table Summarization

Natural language processing (NLP) is a rapidly evolving field that has been used to solve many complex problems. One of the most important tasks in NLP is automatic document summarization, which aims to produce concise summaries that capture the salient information from an input document. However, existing datasets and methods usually focus on text-based content and filter out non-textual elements such as tables, which can limit the informativeness of produced summaries. To address this limitation, Shuaiqi Liu et al. propose FINDSum - the first large-scale dataset for long text and multi-table summarization.

Building FINDSum

FINDSum is built on 21,125 annual reports from 3,794 companies and contains two subsets for summarizing each company's results of operations and liquidity. The dataset includes both textual data from long documents as well as tabular data from dozens of tables in each report. This allows researchers to evaluate how effectively numerical information is included in produced summaries when using different approaches.

Methods Used

To summarize these long texts and multiple tables effectively, Liu et al present three types of summarization methods: extractive-based methods that select important sentences from the input text; abstractive-based methods that generate new sentences based on the input; and hybrid-based methods that combine both extractive and abstractive approaches. Furthermore, they also propose a set of evaluation metrics to assess how accurately numerical information is used in produced summaries.

Results & Analysis

Dataset analyses conducted by Liu et al indicate that jointly considering input textual and tabular data is crucial when summarizing report documents accurately. Experimental results further demonstrate that their proposed hybrid approach outperforms both extractive or abstractive models alone across all evaluation metrics tested on FINDSum’s two subsets - results of operations & liquidity summary tasks respectively .

Conclusion

Overall, this work provides an essential contribution to advancing automatic document summarization by addressing some significant limitations in current approaches while also providing a new benchmark dataset for future research efforts with FINDSum - the first large scale dataset for long text & multi table summarization task..