Short Updates- Machine Learning Based News Summarizer

Automated Text Summarization is becoming important due to the vast amount of data being generated. Manual processing documents tedious, mostly absence standards. Therefore, there a need for mechanism reduce text size, structure it, and make it readable users. Natural Language Processing (NLP) critical analyzing large amounts unstructured, text-heavy data. This project aims address concerns with extractive abstractive summarization by introducing new neural network model that deals repetitive incoherent phrases in longer documents. The incorporates novel Seq2Seq architecture enhances standard attentional an intra-attention mechanism. Additionally, training method combines supervised word prediction reinforcement learning employed. utilizes hybrid pointer-generator network, which distinguishes from encoder-decoder model. approach produces higher quality summaries than existing models.