Deep Neural Network Based Multi-Review Summarization System

E-commerce is prevalent everywhere now-a-days. While shopping from these sites, users generally go through the reviews of the product posted by other users. For a given product, thousands of reviews may be available and it is cumbersome for the user to analyze each and every review. This paper proposes a multi-review summarization method to get a summarized review of products. A deep neural network-based model is employed to create an extractive summary of the reviews collected from online e-commerce sites i.e. Amazon and Flipkart. The deep neural network has been used to obtain the features of the product from multi reviews and cluster the sentences based on learned features. After clustering, a ranking of sentences is done and hence, an extractive summary is generated by selecting top n sentences from each of the clusters formed.

Abstractive Review Summarization based on Improved Attention Mechanism with Pointer Generator Network Model

Nowadays online reviews play an important role by giving an helping hand to the customers to know about other customer’s opinions about the product they are going to purchase. This also guides the organizations as well as government sectors to increase their quality of product and services. So automatic review summarization becomes more important rather than summarizing it manually as it saves time. The aim of this work is to produce a comprehensive summary which includes all key content from the source text. The Proposed Automatic Review Summarization model with improved attention mechanism increases the semantic knowledge and thus improves the summary’s eminence. This encoder-decoder model aims to generate summary in an abstractive way. The Pointer generator mechanism solves the problem of rare words which are out-of-vocabulary and the repetition issues are overcome by coverage mechanism. Experiments were conducted on Amazon’s mobile reviews dataset reveals that the proposed methodology generated more accurate abstractive review summarization when compared with existing techniques. The performance of the summary report is measured using the evaluation metric ROUGE.