Summarization of News Articles Using Transformers

It is essential to provide a better mechanism for swiftly and effectively extracting information in the modern age when there is so much information available online. The summary of a lengthy written document can be difficult for humans to manually extract. There is a variety of textual content available online. Identifying relevant documents in the large amount of available documents and learning crucial information from them are very challenging undertakings. Automatic text summarization is essential for addressing the two issues highlighted above. Text summarization is the process of taking the most important and relevant information from a document or collection of related papers, condensing it, and keeping the main ideas. Abstract Based Text Summarizer takes the input text from the user and provides a summarized text as output. It uses transformers that use thousands of pretrained models to classify the text. Transformers classify the text and Hugging Face is used to arrange those classified texts in a correct order, so that the meaning of the text given as input does not change and the summary would be more effective. The proposed system performs better in terms of rouge parameters.