Text Summarization: GPT Perspective
This study provides an extensive examination of G PT-based models, especially GPT-4, in their ability to effectively condense text and investigates the architectural elements, training approaches, and multiple strategies used by the model to generate high-quality summaries.
Abstract
The field of artificial intelligence (AI) has witnessed significant advancements in recent years, particularly in the areas of machine learning (ML), deep learning (DL), and transformers. These technologies have revolutionized various industries, including healthcare, finance, and transportation. This study provides an extensive examination of GPT-based models, especially GPT-4, in their ability to effectively condense text. We investigate the architectural elements, training approaches, and multiple strategies used by the model to generate high-quality summaries. Our analysis offers valuable insights into the strengths and possible enhancements for GPT-based models in text summarization tasks.