This code demonstrates the use of Natural Language Processing libraries and tools like NLTK and Spacy, and regular expressions. It summarizes a bunch of long texts and new articles into concise and accurate summaries. The code is an application of a Sequence-2-Sequence model, which is designed for tasks involving sequential data. It takes an input sequence, processes it, and generates an output sequence.
Text summarization refers to the technique of condensing a lengthy text document into a well-written summary that captures the essential information and main ideas of the original text, achieved by highlighting the significant points of the document.
The dataset used here is a NEWS SUMMARY dataset from Kaggle. It consists of 4515 examples and contains Author_name, Headlines, Url of Article, Short text, Complete Article.