Environment, Social, and Governance (ESG) KPIs assess an organization's performance on issues such as climate change, greenhouse gas emissions, water consumption, waste management, human rights, diversity, and policies. ESG reports convey this valuable quantitative information through tables. Unfortunately, extracting this information is difficult due to high variability in the table structure as well as content. We propose Statements, a novel domain agnostic data structure for extracting quantitative facts and related information. We propose translating tables to statements as a new supervised deep-learning universal information extraction task. We introduce SemTabNet - a dataset of over 100K annotated tables. Investigating a family of T5-based Statement Extraction Models, our best model generates statements which are 82% similar to the ground-truth (compared to baseline of 21%). We demonstrate the advantages of statements by applying our model to over 2700 tables from ESG reports. The homogeneous nature of statements permits exploratory data analysis on expansive information found in large collections of ESG reports.

通过将表格转化为语句，我们提出了一种新颖的无特定领域数据结构，用于提取定量事实和相关信息，通过研究基于 T5 的语句提取模型，我们的最佳模型与基准模型相比，生成的语句与实际数据相似度达到了 82%，通过将模型应用于 ESG 报告中的超过 2700 个表格，我们展示了语句的优势，允许对大量 ESG 报告中的丰富信息进行探索性数据分析。

基于大型语言模型的ESG指标表格的通用信息提取