Deep learning-based modified transformer model for automated news article summarization

The amount of textual data on the internet is increasing enormously, so data summarization into text has become essential. As generating text summaries manually is an arduous task and humans are generally prone to make mistakes, deep learning techniques have evolved to overcome this problem. Modified transformer-based deep learning models with varying encoder-decoder and feed-forward network layers are proposed to develop an abstractive summary of the news articles. The proposed transformer model provides the advantage of parallelization with the help of multiple attention head layers to process long sentences, and hence, better text summarization performance is achieved. These models are trained on an ?in-shorts? dataset, and the proposed model is compared with the PEGASUS-CNNdaily-mail, BART-large-CNN, and DistilBART-CNN-12-6 models on the CNN/DailyMail dataset. The performance is evaluated in terms of the ROUGE score by comparing it with the existing Recurrent Neural Network (RNN) model. The suggested transformer model achieved a ROUGE score of 0.33, surpassing the RNN model score of 0.17. This innovative approach can be employed on extensive textual data to extract summaries or headlines.