Abstract
This study uses a multilingual summarization model that is based on transformers to justify the United Nations (UN) sustainable development goals (SDGs). The multilingual and immense size of the information related to sustainability renders the synthesis of the related insights challenging policymakers, researchers, and NGOs. To handle this difficulty, we reconfigured a multilingual pre-trained transformer model (mT5) on a new customized set of sustainability texts and news articles, UN reports, and NGO reports in English, Spanish, and French. The methodology flow included the dataset generation, preprocessing, fine-tuning the model, and its thorough evaluation with the help of the automated (ROUGE) and human evaluation. The fine-tuned mT5 was found to be more semantically covered and contextually relevant than the extractive baseline that had the highest ROUGE-L and ROUGE-2 by quantitative analysis, indicating that it achieved a 42% and 39% higher ROUGE-L and ROUGE-2, respectively. The summary generated using human evaluators was rated with a higher level of coherence, fluency, factual accuracy, and SDG relevance (average score 4.2/5) as compared to extractive methods (2.8/5). The outcomes steady the theory that the fine-tuning of domain-specific multilingual processes yields significant improvement in the quality and reliability of automated sustainability summaries. The suggested framework exemplifies the way multilingual natural language processing (NLP) can facilitate access to cross-lingual knowledge and speed up the process of evidence-based decision-making in favor of the SDGs.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Review Article
EUR J SUSTAIN DEV RES, Volume 10, Issue 3, 2026, Article No: em0397
https://doi.org/10.29333/ejosdr/18328
Publication date: 01 Jul 2026
Online publication date: 08 Apr 2026
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