Kurdish Automatic Text Summarization focuses on developing extractive and abstractive summarization frameworks capable of condensing lengthy Kurdish documents into concise, informative summaries while preserving semantic integrity.
Research Approach
The research investigates transformer-based language representations, including Kurdish-adapted BERT and sequence-to-sequence models, to handle Kurdish sentence structures and generate coherent summaries.
Key Features
- Extractive summarization using neural sentence ranking
- Abstractive summarization using encoder-decoder transformers
- Handling multi-document Kurdish news and academic articles
- Automated evaluation using Kurdish-adapted ROUGE metrics
Impact
Automatic summarization enables efficient information assimilation across Kurdish news aggregators, research repositories, and digital archives.