@inproceedings{mousavian-anaraki-etal-2025-unsupervised,
title = "Unsupervised Sustainability Report Labeling based on the integration of the {GRI} and {SDG} standards",
author = "Mousavian Anaraki, Seyed Alireza and
Croce, Danilo and
Basili, Roberto",
editor = "Atwell, Katherine and
Biester, Laura and
Borah, Angana and
Dementieva, Daryna and
Ignat, Oana and
Kotonya, Neema and
Liu, Ziyi and
Wan, Ruyuan and
Wilson, Steven and
Zhao, Jieyu",
booktitle = "Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nlp4pi-1.13/",
doi = "10.18653/v1/2025.nlp4pi-1.13",
pages = "151--162",
ISBN = "978-1-959429-19-7",
abstract = "Sustainability reports are key instruments for communicating corporate impact, but their unstructured format and varied content pose challenges for large-scale analysis. This paper presents an unsupervised method to annotate paragraphs from sustainability reports against both the Global Reporting Initiative (GRI) and Sustainable Development Goals (SDG) standards. The approach combines structured metadata from GRI content indexes, official GRI{--}SDG mappings, and text semantic similarity models to produce weakly supervised annotations at scale. To evaluate the quality of these annotations, we train a multi-label classifier on the automatically labeled data and evaluate it on the trusted OSDG Community Dataset. The results show that our method yields meaningful labels and improves classification performance when combined with human-annotated data. Although preliminary, this work offers a foundation for scalable sustainability analysis and opens future directions toward assessing the credibility and depth of corporate sustainability claims."
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%0 Conference Proceedings
%T Unsupervised Sustainability Report Labeling based on the integration of the GRI and SDG standards
%A Mousavian Anaraki, Seyed Alireza
%A Croce, Danilo
%A Basili, Roberto
%Y Atwell, Katherine
%Y Biester, Laura
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Ignat, Oana
%Y Kotonya, Neema
%Y Liu, Ziyi
%Y Wan, Ruyuan
%Y Wilson, Steven
%Y Zhao, Jieyu
%S Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 978-1-959429-19-7
%F mousavian-anaraki-etal-2025-unsupervised
%X Sustainability reports are key instruments for communicating corporate impact, but their unstructured format and varied content pose challenges for large-scale analysis. This paper presents an unsupervised method to annotate paragraphs from sustainability reports against both the Global Reporting Initiative (GRI) and Sustainable Development Goals (SDG) standards. The approach combines structured metadata from GRI content indexes, official GRI–SDG mappings, and text semantic similarity models to produce weakly supervised annotations at scale. To evaluate the quality of these annotations, we train a multi-label classifier on the automatically labeled data and evaluate it on the trusted OSDG Community Dataset. The results show that our method yields meaningful labels and improves classification performance when combined with human-annotated data. Although preliminary, this work offers a foundation for scalable sustainability analysis and opens future directions toward assessing the credibility and depth of corporate sustainability claims.
%R 10.18653/v1/2025.nlp4pi-1.13
%U https://aclanthology.org/2025.nlp4pi-1.13/
%U https://doi.org/10.18653/v1/2025.nlp4pi-1.13
%P 151-162
Markdown (Informal)
[Unsupervised Sustainability Report Labeling based on the integration of the GRI and SDG standards](https://aclanthology.org/2025.nlp4pi-1.13/) (Mousavian Anaraki et al., NLP4PI 2025)
ACL