  The amount of text data available online is increasing at a very fast pace
hence text summarization has become essential. Most of the modern recommender
and text classification systems require going through a huge amount of data.
Manually generating precise and fluent summaries of lengthy articles is a very
tiresome and time-consuming task. Hence generating automated summaries for the
data and using it to train machine learning models will make these models space
and time-efficient. Extractive summarization and abstractive summarization are
two separate methods of generating summaries. The extractive technique
identifies the relevant sentences from the original document and extracts only
those from the text. Whereas in abstractive summarization techniques, the
summary is generated after interpreting the original text, hence making it more
complicated. In this paper, we will be presenting a comprehensive comparison of
a few transformer architecture based pre-trained models for text summarization.
For analysis and comparison, we have used the BBC news dataset that contains
text data that can be used for summarization and human generated summaries for
evaluating and comparing the summaries generated by machine learning models.
