Pretraining-Based Natural Language Generation for Text Summarization with Machine Learning

Abstract: This paper presents a novel approach to text summarization using pretraining-based natural language generation (NLG) with machine learning. Utilizing transformer models like BERT and GPT, the study develops an encoder-decoder framework to generate concise and coherent summaries from input text. The methodology encompasses data collection, preprocessing, and the implementation of pretraining techniques to enhance language understanding and summary generation. Evaluation using metrics such as ROUGE scores demonstrates the framework's effectiveness in producing high-quality summaries. The research contributes to advancing NLG applications by focusing on the development and evaluation of a robust summarization system. It highlights the benefits of leveraging pretraining methods to improve the efficiency and accuracy of text summarization tasks. By refining the summarization process through advanced NLP techniques, this study aims to provide a comprehensive framework for generating informative summaries across various text domains. Index Terms - Natural Language Generation, Text Summarization, Machine Learning, Transformers, BERT, GPT, ROUGE Scores, Encoder-Decoder Models, Pretraining Techniques.