ESG information extraction with cross-sectoral and multi-source adaptation based on domain-tuned language models

Abstract

Along with future sustainability, factors such as environmental protection, co-prosperity, and ethical management are also gaining attention, resulting in the increased significance of the non-financial performance of companies. Accordingly, many institutions are evaluating the non-financial activities of companies using environmental, social, and governance (ESG) as evaluation indicators. However, the use of the current ESG rating as a reliable indicator has a limitation as the criteria are different for each evaluation institution and the ESG rating for the same company may differ when provided by different evaluation institutions. Accordingly, it is important to directly extract non-financial information generated by various media to provide objective ESG information, but the studies that focus on the need for automatic and direct extraction and classification of ESG information are limited. Therefore, this paper proposes a classifier that can discriminate ESG information. To train the ESG classifier, a dataset was constructed by manually labeling ESG data, and the ESG classifier recorded a classification accuracy of 86.66% for the 4-class classification problem of the constructed dataset. In addition, three application experiments were conducted to verify the usability of the proposed model. First, the results confirmed that the ESG classifier showed a significant performance of 83.96% accuracy for sectors not included in the training data. Second, the results qualitatively confirmed that the proposed model properly extracts ESG-related information from multi-source text data. Finally, the ESG dataset was additionally constructed through the pseudo-labeling technique, and the performance improvement of the data augmentation and classifier was verified. The ESG classifier proposed in this paper will provide appropriate ESG information to stakeholders. In future studies, we intend to extract and classify ESG-related information from more diverse types of data through an advanced ESG classifier. Further, we will use the information for investigating methods for reflecting the non-financial performance of companies in ESG ratings.