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Automatic Question Answering From Large ESG Reports

Author

Listed:
  • Pulkit Parikh

    (VelocityEHS, USA)

  • Julia Penfield

    (VelocityEHS, USA)

Abstract

ESG reports contain crucial information about the corporations' environmental impact, social responsibilities, and governance. Many compliance audits rely on answering questions based on these reports. Moreover, for tasks such as Scope 3 GHG emissions estimation, a company needs to look at the ESG reports of all its typically numerous suppliers to tabulate its own compliance reports. Manually finding specific information from these large documents is immensely time-consuming. This paper presents the first system that automatically answers questions from an ESG report, using advanced machine learning and natural language processing. The proposed system also locates and highlights a cropped screenshot from the report providing the answer. The authors devise two methods for inferring the textual answer, one based on a transformer model pre-trained for extractive question answering and another using a large language model. The task-agnostic method overcomes the challenge of the lengthiness of ESG reports in a cost-effective manner.

Suggested Citation

  • Pulkit Parikh & Julia Penfield, 2024. "Automatic Question Answering From Large ESG Reports," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 20(1), pages 1-21, January.
    • Handle: RePEc:igg:jdwm00:v:20:y:2024:i:1:p:1-21
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.352513
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    References listed on IDEAS

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    1. Bill Edge, 2022. "Recent Developments in Sustainability Reporting," Australian Accounting Review, CPA Australia, vol. 32(2), pages 151-155, June.
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