Long Text and Multi-Table Summarization: Dataset and Method

Shuaiqi Liu, Jiannong Cao, Ruosong Yang, Zhiyuan Wen


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.
Anthology ID:
2022.findings-emnlp.145
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1995–2010
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.145
DOI:
10.18653/v1/2022.findings-emnlp.145
Bibkey:
Cite (ACL):
Shuaiqi Liu, Jiannong Cao, Ruosong Yang, and Zhiyuan Wen. 2022. Long Text and Multi-Table Summarization: Dataset and Method. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1995–2010, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Long Text and Multi-Table Summarization: Dataset and Method (Liu et al., Findings 2022)
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https://aclanthology.org/2022.findings-emnlp.145.pdf
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