Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs
Abstract
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.
Cite
@article{arxiv.2406.19102,
title = {Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs},
author = {Lokesh Mishra and Sohayl Dhibi and Yusik Kim and Cesar Berrospi Ramis and Shubham Gupta and Michele Dolfi and Peter Staar},
journal= {arXiv preprint arXiv:2406.19102},
year = {2024}
}
Comments
Accepted at the NLP4Climate workshop in the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024)