  Document Summarization is the procedure of generating a meaningful and
concise summary of a given document with the inclusion of relevant and
topic-important points. There are two approaches: one is picking up the most
relevant statements from the document itself and adding it to the Summary known
as Extractive and the other is generating sentences for the Summary known as
Abstractive Summarization. Training a machine learning model to perform tasks
that are time-consuming or very difficult for humans to evaluate is a major
challenge. Book Abstract generation is one of such complex tasks. Traditional
machine learning models are getting modified with pre-trained transformers.
Transformer based language models trained in a self-supervised fashion are
gaining a lot of attention; when fine-tuned for Natural Language
Processing(NLP) downstream task like text summarization. This work is an
attempt to use Transformer based techniques for Abstract generation.
