An abstractive summary generation system for customer reviews and news article using deep learning

Abstract

The online customer reviews information available on the internet about any product consider as an essential information resource concerning customer’s interest and their knowledge of the product. It is inscribed in the form of natural language and is unstructured data. To reduce the significant information in the form of a summary is vital to the firms that work on business intelligence. It will help in product recommendation and increase in customer understanding about the product. Therefore, there is much research for creating new methodologies to summarise the text in online customer reviews automatically. In this paper, RNN-Long short-term memory Tensor Flow model along with Recall-Vocabulary Again (RVA) and Copy mechanism has used for the task of generating summary in the form of term wise from the customer reviews and news article. The RNN, along with the RVA mechanism, has been trained through a feed-forward neural network with encoder–decoder to solve the general summarization. The method has validated for the efficiency of the Giga word and DUC dataset to minimize the problem of unknown words in a decoder and generate an accurate summary that contains more vital information.