Machine Learning Approach for Automatic Text Summarization Using Neural Networks

Abstract: Machine learning and deep learning, as we know, have started ruling over almost every field in the computing industry and so, has revolutionized the process of text summarization too. Automatic text summarization is an advancing realm of the natural language processing research in which concise textual summaries are generated from lengthy input documents. Extensive research has been carried out on how automatic summarization can be prosecuted through various extractive and abstractive techniques. In this paper, we address all the approaches to text summarization and present the modus operandi of an Architecture called Encoder-Decoder, under the machine learning approach. Moreover, we propose several novel implementation models for this architecture, in Keras and TensorFlow that consists of various machine learning and deep learning neural network libraries.

Keywords: Machine Learning, Text Summarization, Neural Networks, Deep Learning, Keras, TensorFlow.