A Framework for Abstractive Text Summarization Using Hugging Face Transformers

Discovering information on the internet is now simple, but finding the specific information you're looking for is now more challenging. It can be challenging to read all of the news articles and other texts that are available online. Thus, the need for effective text summarization has never been more important than it is in today's age of information abundance. Text summarization can be useful in reducing the length of these texts. Finding the information needed and comprehending the main ideas of a document are made simpler as a result. However, humans are unable to, or lack the resources to manually read and summarize the enormous number of documents, reports, or news articles that are generated each and every day. Thus, Automated, or Autonomous Text Summarization backed by Natural Language Processing (NLP) can significantly help in this regard. This model, Autonomous Document Summarization is one such model that uses Google's T5-Small to train a Sequence-to-Sequence (RNN) based model that can generate abstractive text summaries.