TEXT SUMMARIZATION USING MACHINE LEARNING AND DEEP NEURAL NETWORK

Abstract

The creation of summaries and their evaluation have grown in popularity over time. The two primary techniques for creating summaries from a given text corpus are extractive text summarization and abstractive text summarization. To create abstractive and extractive summaries, a variety of techniques and methodologies are available. Using machine learning and deep neural networks, an automatic extractive and abstractive text summarization technique is developed. To produce a summary from a given text, the Extractive model utilizes (Spacy, TF-IDF Vectorizer, and Latent semantic Analysis algorithm (LSA)) and the Abstract model employs (Encoder-decoder RNN with LSTM units and attention mechanism). The goal of the model is to demonstrate the benefits of applying the Extractive and Abstractive approach to text summarization in practical settings.