"siNews : A Domain-Specific Hybrid Approach for Summarizing  Low-Resource Sinhala News"

Paris, Mahipala Kevin

dc.contributor.author	Paris, Mahipala Kevin
dc.date.accessioned	2026-05-04T10:20:08Z
dc.date.available	2026-05-04T10:20:08Z
dc.date.issued	2025
dc.identifier.citation	"Paris, Mahipala Kevin (2025) siNews : A Domain-Specific Hybrid Approach for Summarizing Low-Resource Sinhala News. BSc. Dissertation, Informatics Institute of Technology"	en_US
dc.identifier.issn	20210937
dc.identifier.uri	http://dlib.iit.ac.lk/xmlui/handle/123456789/3254
dc.description.abstract	A large number of news articles are created and published every day, and users can't browse through all available news to seek their interested news information. There is a lack of standardized datasets for Sinhala, which makes it challenging to train and evaluate NLP models effectively. A combination of extractive and abstractive summarization with domain-specific fine-tuned pre-trained model. The model is trained with a custom-made, domain-specifically categorized data set to get the characteristics of each news domain.	en_US
dc.language.iso	en	en_US
dc.subject	Machine Learning	en_US
dc.subject	Text Summarization	en_US
dc.title	"siNews : A Domain-Specific Hybrid Approach for Summarizing Low-Resource Sinhala News"	en_US
dc.type	Thesis	en_US