Text Summarization using NLP

The method of automatically producing succinct and cohesive summaries from longer textual materials by utilising Natural Language Processing (NLP) is known as text summarisation. This technique captures the essential concepts while cutting down on the length of the information. NLP-based summarisation can be done in two ways: extractively and abstractively. Using the actual terminology, extractive summarisation chooses key lines or phrases straight from the book. On the other hand, abstractive summarisation, which frequently calls for a higher level of language comprehension, creates new phrases to convey the main concepts. While transformer-based models like BERT and GPT are popular for abstractive summaries because they can learn contextual language nuances, NLP approaches like tokenisation, sentence rating, and vectorisation are utilised for extractive summaries. Applications for text summarisation include digital assistants, research paper summarisation, news aggregation, and customer feedback analysis.