Empirical Evaluation of Pre-trained Language Models for Summarizing Moroccan Darija News Articles
Year: 2025
Type: conference-paper
Abstract: Moroccan Dialect (MD), or "Darija," is a primary spoken variant of Arabic in Morocco, yet remains underrepresented in Natural Language Processing (NLP) research, particularly in tasks like summarization.Despite a growing volume of MD textual data online, there is a lack of robust resources and NLP models tailored to handle the unique linguistic challenges posed by MD.In response, we introduce GOOD.MA_v2, an expanded version of the GOUD.MA dataset, containing over 50k articles with their titles across 11 categories.This dataset provides a more comprehensive resource for developing summarization models.We evaluate the application of large language models (LLMs) for MD summarization, utilizing both fine-tuning and zero-shot prompting with encoder-decoder and causal LLMs, respectively.Our findings demonstrate that an expanded dataset improves summarization performance and highlights the capabilities of recent LLMs in handling MD text.We open-source our dataset, fine-tuned models, and all experimental code, establishing a foundation for future advancements in MD NLP.We release the code at https://github.com/AzzedineAftiss/Moroccan- Dialect-Summarization.
Authors: Aftiss, Azzedine, LAMSIYAH, Salima, SCHOMMER, Christoph, El Alaoui, Said Ouatik
Institutions: Université Ibn-Tofail, University of Luxembourg
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7110486887