Deep Learning Approaches to Multimodal Sustainable Report Analysis

Abstract

This review paper explores the application of deep learning techniques for analyzing multimodal sustainable reports, with a particular focus on enhancing categorization accuracy and deriving deeper ESG insights. Sustainable reporting integrates environmental, social, and governance (ESG) data, often presenting information in diverse formats, including text, tables, images, and charts. Traditional analysis methods, relying heavily on manual coding and keyword-based algorithms, struggle with the complexity, heterogeneity, and dynamic nature of such data, leading to inaccuracies and inefficiencies. Deep learning, with its capacity to learn intricate patterns from high-dimensional and varied data sources, offers promising avenues for automated, comprehensive, and insightful analysis. This paper provides an overview of existing deep learning models (e.g., Transformer Models, Recurrent Neural Networks, Convolutional Neural Networks, Fully Connected Neural Networks) and architectures pertinent to multimodal data integration and analysis, identifies current challenges and limitations in their application to sustainable reports (such as data scarcity and the need for explainability), and proposes future research directions to enhance the efficiency and accuracy of ESG data extraction and interpretation, aiming to establish a standard for automatic sustainability report processing