- Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi
- Cilt: 32 Sayı: 1
- NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION
NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION
Authors : Yunus Emre Işıkdemir
Pages : 1140-1151
Doi:10.31796/ogummf.1303569
View : 138 | Download : 236
Publication Date : 2024-04-22
Article Type : Research
Abstract :As the amount of the available information continues to grow, finding the relevant information has become increasingly challenging. As a solution, text summarization has emerged as a vital method for extracting essential information from lengthy documents. There are various techniques available for filtering documents and extracting the pertinent information. In this study, a comparative analysis is conducted to evaluate traditional approaches and state-of-the-art methods on the BBC News and CNN/DailyMail datasets. This study offers valuable insights for researchers to advance their research and helps practitioners in selecting the most suitable techniques for their specific use cases.Keywords : Metin Özetleme, Transformer, Doğal Dil İşleme, Büyük Dil Modelleri, Derin Öğrenme