  People nowadays use search engines like Google, Yahoo, and Bing to find
information on the Internet. Due to explosion in data, it is helpful for users
if they are provided relevant summaries of the search results rather than just
links to webpages. Text summarization has become a vital approach to help
consumers swiftly grasp vast amounts of information.In this paper, different
pre-trained models for text summarization are evaluated on different datasets.
Specifically, we have used three different pre-trained models, namely,
google/pegasus-cnn-dailymail, T5-base, facebook/bart-large-cnn. We have
considered three different datasets, namely, CNN-dailymail, SAMSum and BillSum
to get the output from the above three models. The pre-trained models are
compared over these different datasets, each of 2000 examples, through ROUGH
and BLEU metrics.
