We often come across large texts for example, stories, news articles etc. Sometimes it is required to have a quick summary of the context to save our time in reading the long paragraphs. In literature we often know this process by the name of Text Summarization. Text summarization comes under a field in Data Science called as Natural Language Processing. Over the years, a lot of work towards Text Summarization has been presented on English Corpus, but not much work has been done for our Indian Languages like Hindi. Our goal for this research work is to find a model which is able to generate good Hindi summary out of Hindi texts and is efficient in terms of time and resources. To be able to achieve our goal, we have performed a comparative analysis of the Deep Learning Models for text summarization so that we can compare the models on various parameters and their results. In our paper, we have trained the models BART and T5 on the ‘Hindi Text Short Summarization’ Corpus where T5 performed better than BART and with a rouge-L (f-measure) score of 20.5. For experimentation, we have used the ‘Hindi Text Short Summarization’ Corpus available from Kaggle as not much work has been performed until now on this dataset and we wanted to learn about the essential data transformations or data pre-processing that can be done on a Hindi dataset so that our model yields us good results for the Hindi Corpus as most of times the industrial data are not very clean and understanding the data and performing data-preprocessing becomes a very crucial step.