Business Problem: Readers frequently do not have time to read entire articles, and reading merely the headline and subheadings does not provide them with a complete picture of the content. News organizations such as the Associated Press, Bloomberg, and Reuters are actively trying to automate stories in areas such as finance and sports. It is hard for news organizations to produce summaries for every piece they publish. As a result, having in-built tools that summarize stories for users may be a good idea for news apps.
The project's goal is to use different Deep Learning techniques - T5 Transformer, Encoder & Decoder with BiLSTM models, and NLP to generate coherent summaries – to generate brief descriptions of news stories.
Model accuracy of Encoder & Decoder using BiLSTM and Keras embedding layer was 46%. However, summaries generated by pre-trained T5 Transformer were more precise.