The “Multilingual Corpus of World’s Constitutions” (MCWC) serves as a valuable resource for the NLP community, offering a comprehensive collection of constitutions from around the world. Its focus on data quality and breadth of coverage enables advanced research in constitutional analysis, machine translation, and cross-lingual legal studies. The MCWC prepares its data to ensure high quality and minimal noise, while also providing valuable mappings of constitutions to their respective countries and continents, facilitating comparative analysis. Notably, the corpus offers pairwise sentence alignments across languages, supporting machine translation experiments. We utilise a leading Machine Translation model, fine-tuned on the MCWC to achieve accurate and context-aware translations. Additionally, we introduce an independent Machine Translation model as a comparative baseline. Fine-tuning the model on the MCWC improves accuracy, highlighting the significance of such a legal corpus for NLP and Machine Translation. The MCWC’s rich multilingual content and rigorous data quality standards raise the bar for legal text analysis and inspire innovation in the NLP community, opening new avenues for studying constitutional texts and multilingual data analysis.

This research introduces DARES, a dataset for assessing the readability of Arabic text in Saudi school materials. DARES compromise of 13335 instances from textbooks used in 2021 and contains two subtasks; (a) Coarse-grained readability assessment where the text is classified into different educational levels such as primary and secondary. (b) Fine-grained readability assessment where the text is classified into individual grades.. We fine-tuned five transformer models that support Arabic and found that CAMeLBERTmix performed the best in all input settings. Evaluation results showed high performance for the coarse-grained readability assessment task, achieving a weighted F1 score of 0.91 and a macro F1 score of 0.79. The fine-grained task achieved a weighted F1 score of 0.68 and a macro F1 score of 0.55. These findings demonstrate the potential of our approach for advancing Arabic text readability assessment in education, with implications for future innovations in the field.

Podcasts have become increasingly popular in recent years, resulting in a massive amount of audio content being produced every day. Efficient summarisation of podcast episodes can enable better content management and discovery for users. In this paper, we explore the use of abstractive text summarisation methods to generate high-quality summaries of podcast episodes. We use pre-trained models, BART and T5, to fine-tune on a dataset of Spotify’s 100K podcast. We evaluate our models using automated metrics and human evaluation, and find that the BART model fine-tuned on the podcast dataset achieved a higher ROUGE-1 and ROUGE-L score compared to other models, while the T5 model performed better in terms of semantic meaning. The human evaluation indicates that both models produced high-quality summaries that were well received by participants. Our study demonstrates the effectiveness of abstractive summarisation methods for podcast episodes and offers insights for improving the summarisation of audio content.