With the proliferation of blogs, news stories, and reports, the extraction of useful information from this extensive collection of textual documents is a tedious task. Automatic text summarization provides an effective solution for condensing these documents into concise summaries while preserving essential information and meaning. Numerous noteworthy summarization models have been proposed to address various challenges, including saliency, fluency, human readability, and the generation of high-quality summaries. In this study, we introduce the Text-To-Text Transfer Transformer (T5) model for the task of abstractive summarization with knowledge representation. The experimental results demonstrate that the T5 model produces summaries that are more conceptual, comprehensible, and abstractive. To assess the quality of the generated summaries, we consider the ROUGE and BLEU scores.