SustainableQA: A Comprehensive Question Answering Dataset for Corporate Sustainability and EU Taxonomy Reporting

• URL: http://arxiv.org/abs/2508.03000v1
• Date: Tue, 05 Aug 2025 02:03:59 GMT
• Title: SustainableQA: A Comprehensive Question Answering Dataset for Corporate Sustainability and EU Taxonomy Reporting
• Authors: Mohammed Ali, Abdelrahman Abdallah, Adam Jatowt,
• Abstract summary: We introduce SustainableQA, a novel dataset and a scalable pipeline for generating a comprehensive QA datasets from corporate sustainability reports and annual reports.<n>With over 195,000 diverse factoid and non-factoid QA pairs, SustainableQA is an effective resource for developing and benchmarking advanced knowledge assistants.
• Score: 16.86139440201837
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The growing demand for corporate sustainability transparency, particularly under new regulations like the EU Taxonomy, necessitates precise data extraction from large, unstructured corporate reports. Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems, requires high-quality, domain-specific question-answering (QA) datasets to excel at particular domains. To address this, we introduce SustainableQA, a novel dataset and a scalable pipeline for generating a comprehensive QA datasets from corporate sustainability reports and annual reports. Our approach integrates semantic chunk classification, a hybrid span extraction pipeline combining fine-tuned Named Entity Recognition (NER), rule-based methods, and LLM-driven refinement, alongside a specialized table-to-paragraph transformation. With over 195,000 diverse factoid and non-factoid QA pairs, SustainableQA is an effective resource for developing and benchmarking advanced knowledge assistants capable of navigating complex sustainability compliance

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