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

Yunus Emre Işıkdemir

doi:10.31796/ogummf.1303569

TR EN

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

Öz

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.

Anahtar Kelimeler

Text Summarization, Transformers, NLP, LLM, Deep Learning

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Bilgisayar Yazılımı

Bölüm

Araştırma Makalesi

Yazarlar

Yunus Emre Işıkdemir ^*
0000-0001-7022-2854
Türkiye

Erken Görünüm Tarihi

22 Nisan 2024

Yayımlanma Tarihi

22 Nisan 2024

Gönderilme Tarihi

27 Mayıs 2023

Kabul Tarihi

12 Mart 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 32 Sayı: 1

DOI

https://doi.org/10.31796/ogummf.1303569

IZ

https://izlik.org/JA53FK97UW

APA

Işıkdemir, Y. E. (2024). NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 32(1), 1140-1151. https://doi.org/10.31796/ogummf.1303569

AMA

1.Işıkdemir YE. NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION. ESOGÜ Müh Mim Fak Derg. 2024;32(1):1140-1151. doi:10.31796/ogummf.1303569

Chicago

Işıkdemir, Yunus Emre. 2024. “NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 32 (1): 1140-51. https://doi.org/10.31796/ogummf.1303569.

EndNote

Işıkdemir YE (01 Nisan 2024) NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 32 1 1140–1151.

IEEE

[1]Y. E. Işıkdemir, “NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION”, ESOGÜ Müh Mim Fak Derg, c. 32, sy 1, ss. 1140–1151, Nis. 2024, doi: 10.31796/ogummf.1303569.

ISNAD

Işıkdemir, Yunus Emre. “NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 32/1 (01 Nisan 2024): 1140-1151. https://doi.org/10.31796/ogummf.1303569.

JAMA

1.Işıkdemir YE. NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION. ESOGÜ Müh Mim Fak Derg. 2024;32:1140–1151.

MLA

Işıkdemir, Yunus Emre. “NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, c. 32, sy 1, Nisan 2024, ss. 1140-51, doi:10.31796/ogummf.1303569.

Vancouver

1.Yunus Emre Işıkdemir. NLP TRANSFORMERS: ANALYSIS OF LLMS AND TRADITIONAL APPROACHES FOR ENHANCED TEXT SUMMARIZATION. ESOGÜ Müh Mim Fak Derg. 01 Nisan 2024;32(1):1140-51. doi:10.31796/ogummf.1303569

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Leveraging LLMs for Smart Cities Qualitative Data Analysis

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https://doi.org/10.1002/cpe.70547

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

TRANSFORMER MİMARİSİ TABANLI METİN ÖZETLEME VE DOĞAL DİL ANLAMLANDIRMA

Öz

Anahtar Kelimeler

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

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Erken Görünüm Tarihi

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

Leveraging LLMs for Smart Cities Qualitative Data Analysis