AI-driven Generation of News Summaries: Leveraging GPT and Pegasus Summarizer for Efficient Information Extraction
Résumé
The escalating volume of online information presents a formidable challenge in swiftly accessing pertinent news. The profusion of articles across diverse sources necessitates however automated methods for efficient extraction and summarization of information. In response to this challenge, our research delves into the design and implementation of an online press synthesis tool, harnessing advanced artificial intelligence models. This paper explores the viability of an AI-based online press synthesis tool incorporating two pre-trained models, GPT-3.5 Turbo 16k and Pegasus Summarizer. These models generate highquality automatic summaries from scraped articles on various news sites. Our methodology encompasses a robust web scraping process utilizing Beautiful Soup, integration of pre-trained models, calculation of article similarity, and audio matching of produced summaries. Evaluation metrics such as ROUGE or BLEU gauge model performance. Experimentation results reveal the GPT-3.5 Turbo 16k model's superior accuracy. In a test involving a renowned article on the declaration of human rights, GPT-3.5 Turbo 16k achieved a BLEU score of 16.39% and a ROUGE score of 0.66%. Notably, the turner007/pegasus-summarizer model demonstrated significant performance, registering a BLEU score of 15.45% and a ROUGE score of 0.45%. These scores were obtained through tests on reference summaries authored by humans. Beyond the results, we identify potential areas for enhancement, including enriching the database with expert-authored human summaries and developing a dynamic approach to adapt to evolving news developments. Furthermore, we explore the unified models approach. In conclusion, our work lays the groundwork for an advanced AI-powered online press synthesis tool, paving the way for continuous improvements in an ever-evolving media landscape.
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