AI-driven Generation of News Summaries Leveraging GPT and Pegasus Summarizer for Efficient Information Extraction

The surge of online information makes it challenging to access relevant news quickly. This necessitates automated methods to effectively extract and summarize information. Our research focuses on designing an online press synthesis tool using advanced AI models. We investigate the feasibility of employing two pre-trained models, GPT-3.5 Turbo 16k and Pegasus Summarizer, to generate high-quality summaries from scraped articles. Our methodology encompasses robust web scraping, model integration, and metric evaluation. Experimental results demonstrate that GPT-3.5 Turbo 16k outperforms in accuracy, achieving a BLEU score of 16.39% and a ROUGE score of 0.66%. The turner007/pegasus-summarizer model also performs well, with a BLEU score of 15.45% and a ROUGE score of 0.45%. We identify areas for improvement, such as enriching the database with expert-authored summaries and adopting a dynamic approach to news adaptation. Additionally, we explore a unified models approach for further refinement.