DEEP WRAP-UP- Automatic Document Summarization with Animations

Summarization is an ongoing field of study with the objective of providing a concise and useful gist from vast amounts of content. Everything is digitalized these days, and digital data is utilized for various purposes on the Internet. There are diverse forms of content, ranging from different formats to domains and topics. E-learners find it relatively tough to read and understand the lengthy content. To address the aforementioned challenge, this research study has proposed a novel summarization approach based on different characteristics and features of summaries that can flexibly switch between extractive, abstractive, and illustrative summaries with images and videos. The proposed Deep Wrap Up provides extractive summary by using the NLTK (Natural Language Toolkit) library, abstractive summary using T5 (Text-to-Text Transfer) transformers, summary with images using the CLIP (Contrastive Language-Image Pre-Training) library, and summary with videos generated using moviePy. As a result, our technique successfully combines the benefits of several branches of summarization, balancing informativeness and conciseness.