@article{article_1303569, title={NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION}, journal={Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi}, volume={32}, pages={1140–1151}, year={2024}, DOI={10.31796/ogummf.1303569}, author={Işıkdemir, Yunus Emre}, keywords={Text Summarization, Transformers, NLP, LLM, Deep Learning}, 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.}, number={1}, publisher={Eskişehir Osmangazi University}