NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION

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Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi
Volume:32 Issue: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 : 141 | Download : 241

Publication Date : 2024-04-22

Article Type : Research Paper

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

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