NLP-Enhanced Long Document Summarization: A Comprehensive Approach for Information Condensation

The present era is characterized by information overload, making the ability to distil multitude of data into concise and meaningful summaries very important. The response to ever-increasing challenge is this revolutionary NLP based model called text Summarizer that will redefine the interactions with information. Text Summarizer aims at providing users with quick, accurate and coherent summaries of lengthy texts so to promote their understanding. The cutting-edge methods harnessed by the Text Summarizer enable it to assimilate given text context, identify major themes and provide brief summaries that capture the essence of source content. To build this text summarizer advanced NLP algorithms are used. Modern deep learning models are mainly based on transformer architectures like BERT (Bidirectional Encoder Representations from Transformers) or GPT (Generative Pre-trained Transformer); they are used because they have the capacity to capture complicated language patterns and nuances. Transformer is a sequence-to-sequence model which uses attention technique to process text sequentially. It is made up of encoders and decoders interconnection. This transformer takes in the text input and provide the summary as output. Transformer discussed in this paper has an accuracy of around 98% which is trained over cnn-dailymail dataset.