Summary Generation Using Deep Learning

Summarization is the process of converting any given long text into a lossy compressed version while preserving the overall essence and meaning of the original given text. A problem in natural language processing is the task of summarizing a voluminous text. This research, lays emphasis on abstractive summarization of the text, a process which generates a coherent, short and meaningful text by learning the context from given input source text. This work is a preliminary attempt to generate abstractive summary. The proposed work uses the Attentional Encoder and Decoder based Sequence to Sequence Recurrent Neural Network model. The proposed research’s primary objective is to get insights on the working of the mentioned Sequence architecture and the intricacies of different subcomponents of the encoders and the decoders architecture working together so as to give a particular end result. The scope of this paper covers an experimentation of performing text summarization process using Encoder and Decoder based Sequential Learning Recurrent Neural Network design. Satisfactory summary results are obtained after the model is experimented and analyzed on the Amazon Fine Food reviews.