Sustainability metrics have increasingly become a crucial non-financial criterion in investment decision-making. Organizations worldwide are recognizing the importance of sustainability and are proactively highlighting their efforts through specialized sustainability reports. Unlike traditional annual reports, these sustainability disclosures are typically text-heavy and are often expressed as infographics, complex tables, and charts. The non-machine-readable nature of these reports presents a significant challenge for efficient information extraction. The rapid advancement of Vision Language Models (VLMs) has raised the question whether these VLMs can address such challenges in domain specific task. In this study, we demonstrate the application of VLMs for extracting sustainability information from dedicated sustainability reports. Our experiments highlight the limitations in the performance of several open-source VLMs in extracting information about sustainability disclosures from different type of pages.

Advanced neural network architectures have provided several opportunities to develop systems to automatically capture information from domain-specific unstructured text sources. The FinSim4-ESG shared task, collocated with the FinNLP workshop, proposed two sub-tasks. In sub-task1, the challenge was to design systems that could utilize contextual word embeddings along with sustainability resources to elaborate an ESG taxonomy. In the second sub-task, participants were asked to design a system that could classify sentences into sustainable or unsustainable sentences. In this paper, we utilize semantic similarity features along with BERT embeddings to segregate domain terms into a fixed number of class labels. The proposed model not only considers the contextual BERT embeddings but also incorporates Word2Vec, cosine, and Jaccard similarity which gives word-level importance to the model. For sentence classification, several linguistic elements along with BERT embeddings were used as classification features. We have shown a detailed ablation study for the proposed models.