Abstract:

Abstract 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 major challenge. Book summarization is one of the complex tasks which is time consuming as well.  Traditional machine learning models are getting modified with pre-trained transformers. Transformer based Language models trained in a self-supervised fashion 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 technique for Book Summarization.

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