A nifty review to text summarization-based recommendation system for electronic products.

With the commencement of new technology, demands of online shopping are increasing day by day and hence an electronic product receives a huge number of customers reviews everyday. Because of this, a customer who wants to buy a particular product face difficulty as he needs to go through all the reviews of that product before taking a final decision. Automatically generated summary of the reviews could aid the customers in selecting the appropriate product. Aiming in this direction, a novel approach for making automatic extractive text summaries of the reviews for various electronic products is proposed in this paper. We have taken into account both the content of the review and the author's credibility while evaluating the importance of a sentence. Both the content and semantic similarities are measured between every pair of sentences of a review. In order to form the summary of the reviews, fuzzy c-means clustering is used. For experimental purpose, Amazon dataset is used and the results indicate that the proposed method outperforms some of the baseline methods for generating the summary of the reviews, thus providing more concrete and robust summary. [ABSTRACT FROM AUTHOR]