TRANSFORMER MİMARİSİ TABANLI METİN ÖZETLEME VE DOĞAL DİL ANLAMLANDIRMA

Yıl 2024, , 1140 - 1151, 22.04.2024

Yunus Emre Işıkdemir

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

Öz

Hızla büyüyen yüksek hacimli bilgi kaynaklarından faydalı bilgilere erişim, giderek zorlaşmaktadır. Bu soruna çözüm olarak geliştirilen metin özetleme yöntemleri, yüksek hacimli belgelerden önemli bilgilerin çıkarılmasında önemli bir role sahiptir. Belgeleri filtreleme ve ilgili bilgileri çıkarma amacıyla çeşitli teknikler mevcuttur. Bu çalışmada, BBC News ve CNN/DailyMail veri setleri üzerinde, geleneksel yaklaşımlar ile en güncel yöntemlerin karşılaştırmalı analizini sunmaktadır. Araştırmacılara ilerlemelerine katkı sağlayacak değerli bilgiler sunmakta ve uygulayıcıların özel kullanım durumlarına en uygun teknikleri seçmelerine yardımcı olmaktadır.

Anahtar Kelimeler

Metin Özetleme, Transformer, Doğal Dil İşleme, Büyük Dil Modelleri, Derin Öğrenme

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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