  Text summarization is a critical Natural Language Processing (NLP) task with
applications ranging from information retrieval to content generation.
Leveraging Large Language Models (LLMs) has shown remarkable promise in
enhancing summarization techniques. This paper embarks on an exploration of
text summarization with a diverse set of LLMs, including MPT-7b-instruct,
falcon-7b-instruct, and OpenAI ChatGPT text-davinci-003 models. The experiment
was performed with different hyperparameters and evaluated the generated
summaries using widely accepted metrics such as the Bilingual Evaluation
Understudy (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 the
others. This investigation involved two distinct datasets: CNN Daily Mail and
XSum. Its primary objective was to provide a comprehensive understanding of the
performance of Large Language Models (LLMs) when applied to different datasets.
The assessment of these models' effectiveness contributes valuable insights to
researchers and practitioners within the NLP domain. This work serves as a
resource for those interested in harnessing the potential of LLMs for text
summarization and lays the foundation for the development of advanced
Generative AI applications aimed at addressing a wide spectrum of business
challenges.
