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Text summarization is a critical Natural Language Processing (NLP) task withapplications ranging from information retrieval to content generation.Leveraging Large Language Models (LLMs) has shown remarkable promise inenhancing summarization techniques. This paper embarks on an exploration oftext summarization with a diverse set of LLMs, including MPT-7b-instruct,falcon-7b-instruct, and OpenAI ChatGPT text-davinci-003 models. The experimentwas performed with different hyperparameters and evaluated the generatedsummaries using widely accepted metrics such as the Bilingual EvaluationUnderstudy (BLEU) Score, Recall-Oriented Understudy for Gisting Evaluation(ROUGE) Score, and Bidirectional Encoder Representations from Transformers(BERT) Score. According to the experiment, text-davinci-003 outperformed theothers. This investigation involved two distinct datasets: CNN Daily Mail andXSum. Its primary objective was to provide a comprehensive understanding of theperformance of Large Language Models (LLMs) when applied to different datasets.The assessment of these models' effectiveness contributes valuable insights toresearchers and practitioners within the NLP domain. This work serves as aresource for those interested in harnessing the potential of LLMs for textsummarization and lays the foundation for the development of advancedGenerative AI applications aimed at addressing a wide spectrum of businesschallenges.

Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI Chat-GPT Models