Advanced Unstructured Data Processing for ESG Reports: A Methodology for Structured Transformation and Enhanced Analysis

• URL: http://arxiv.org/abs/2401.02992v1
• Date: Thu, 4 Jan 2024 06:26:59 GMT
• Title: Advanced Unstructured Data Processing for ESG Reports: A Methodology for Structured Transformation and Enhanced Analysis
• Authors: Jiahui Peng, Jing Gao, Xin Tong, Jing Guo, Hang Yang, Jianchuan Qi, Ruiqiao Li, Nan Li, Ming Xu
• Abstract summary: This study introduces an innovative methodology to transform ESG reports into structured, analyzable formats. Our approach offers high-precision text cleaning, adept identification and extraction of text from images, and standardization of tables within these reports. This research marks a substantial contribution to the fields of industrial ecology and corporate sustainability assessment.
• Score: 20.038120319271773
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the evolving field of corporate sustainability, analyzing unstructured Environmental, Social, and Governance (ESG) reports is a complex challenge due to their varied formats and intricate content. This study introduces an innovative methodology utilizing the "Unstructured Core Library", specifically tailored to address these challenges by transforming ESG reports into structured, analyzable formats. Our approach significantly advances the existing research by offering high-precision text cleaning, adept identification and extraction of text from images, and standardization of tables within these reports. Emphasizing its capability to handle diverse data types, including text, images, and tables, the method adeptly manages the nuances of differing page layouts and report styles across industries. This research marks a substantial contribution to the fields of industrial ecology and corporate sustainability assessment, paving the way for the application of advanced NLP technologies and large language models in the analysis of corporate governance and sustainability. Our code is available at https://github.com/linancn/TianGong-AI-Unstructure.git.

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