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    <abstract>Sustainability reporting has become an annual requirement in many countries and for certain types of companies. Sustainability reports inform stakeholders about companies’ commitment to sustainable development and their economic, social, and environmental sustainability practices. However, the fact that norms and standards allow a certain discretion to be adopted by drafting organizations makes such reports hardly comparable in terms of layout, disclosures, key performance indicators (KPIs), and so on. In this work, we present a system based on natural language processing and information extraction techniques to retrieve relevant information from sustainability reports, compliant with the Global Reporting Initiative Standards, written in Italian and English language. Specifically, the system is able to identify references to the various sustainability topics discussed by the reports: on which page of the document those references have been found, the context of each reference, and if it is mentioned positively or negatively. The output of the system has been then evaluated against a ground truth obtained through a manual annotation process on 134 reports. Experimental outcomes highlight the affordability of the approach for improving sustainability disclosures, accessibility, and transparency, thus empowering stakeholders to conduct further analysis and considerations.</abstract>
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%0 Conference Proceedings
%T An NLP Approach for the Analysis of Global Reporting Initiative Indexes from Corporate Sustainability Reports
%A Polignano, Marco
%A Bellantuono, Nicola
%A Lagrasta, Francesco Paolo
%A Caputo, Sergio
%A Pontrandolfo, Pierpaolo
%A Semeraro, Giovanni
%Y Wan, Mingyu
%Y Huang, Chu-Ren
%S Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F polignano-etal-2022-nlp
%X Sustainability reporting has become an annual requirement in many countries and for certain types of companies. Sustainability reports inform stakeholders about companies’ commitment to sustainable development and their economic, social, and environmental sustainability practices. However, the fact that norms and standards allow a certain discretion to be adopted by drafting organizations makes such reports hardly comparable in terms of layout, disclosures, key performance indicators (KPIs), and so on. In this work, we present a system based on natural language processing and information extraction techniques to retrieve relevant information from sustainability reports, compliant with the Global Reporting Initiative Standards, written in Italian and English language. Specifically, the system is able to identify references to the various sustainability topics discussed by the reports: on which page of the document those references have been found, the context of each reference, and if it is mentioned positively or negatively. The output of the system has been then evaluated against a ground truth obtained through a manual annotation process on 134 reports. Experimental outcomes highlight the affordability of the approach for improving sustainability disclosures, accessibility, and transparency, thus empowering stakeholders to conduct further analysis and considerations.
%U https://aclanthology.org/2022.csrnlp-1.1
%P 1-8

Markdown (Informal)

[An NLP Approach for the Analysis of Global Reporting Initiative Indexes from Corporate Sustainability Reports](https://aclanthology.org/2022.csrnlp-1.1) (Polignano et al., CSRNLP 2022)

• An NLP Approach for the Analysis of Global Reporting Initiative Indexes from Corporate Sustainability Reports (Polignano et al., CSRNLP 2022)

ACL

• Marco Polignano, Nicola Bellantuono, Francesco Paolo Lagrasta, Sergio Caputo, Pierpaolo Pontrandolfo, and Giovanni Semeraro. 2022. An NLP Approach for the Analysis of Global Reporting Initiative Indexes from Corporate Sustainability Reports. In Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference, pages 1–8, Marseille, France. European Language Resources Association.