Deep learning-based extractive and abstractive summarization for the Azerbaijani language

Abstract

This article investigates extractive and abstractive text summarization for the Azerbaijani language, a low-resource and underrepresented language in natural language processing. While the underlying modeling approaches are well established, their application to Azerbaijani summarization remains largely unexplored due to the scarcity of large-scale datasets and prior empirical studies. To address this gap, we conduct a systematic evaluation of both extractive methods based on sentence ranking and an abstractive approach using a fine-tuned mT5-base model. Our experiments are carried out on a large-scale dataset comprising over 115,000 Azerbaijani news articles paired with human-written summaries. The models are evaluated using standard automatic metrics, including Recall-Oriented Understudy for Gisting Evaluation (ROUGE), Bilingual Evaluation Understudy (BLEU), and Metric for Evaluation of Translation with Explicit ORdering (METEOR), yielding strong results that highlight the benefits of task specific fine-tuning for abstractive summarization, while also demonstrating the competitiveness of extractive baselines. In addition, we analyze the impact of long input sequences and discuss architectural and dataset-related limitations affecting performance. Overall, this study provides a comprehensive empirical baseline for Azerbaijani text summarization and serves as a reference point for future research in low-resource summarization and related Azerbaijani Natural Language Processing (NLP) applications.