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The process of summarization is taking a longer piece of material and reducing it to a shorter version without losing any of the essential information or meaning. Due to the exponential increase in available information and data, automatic text summarizing has emerged as a useful alternative to the time-consuming and error-prone process of manual summation of massive amounts of texts. The summaries the algorithm generates help users better understand the material presented in the original document. There are two main types of summarization: abstract and extractive. The number of available automatic summarization tools for Indian languages is low. Our focus in this area has been on creating an automatic English-language text summarizer utilizing the T5 transformer model; we've employed a manual dataset for testing and training. Here we have used news summary dataset. [ABSTRACT FROM AUTHOR]