The Internet has become popular and convenient. Product articles and reviews are written by people on digital media platforms such as Facebook, PTT, Mobile01, and Apple Daily News. Most people read many articles and reviews on digital media when they want to buy a product. However, an overwhelming number of articles and reviews of products is available on the Internet, and a prospective buyer can become confused. People should organize these articles and reviews before deciding on whether to buy a product or not. Therefore, it is essential to summarize the available data and provide customers useful information.Many researchers have investigated this task, but most studies have been focused on English reviews. This work focuses on Chinese articles and reviews in digital media, and propose a system designed to summarize data from digital media. When a user is interested in a product, the system extracts features and opinion words of the product from review articles and uses these features to identify sentences highly related to the product. After obtaining these sentences, the approach in this work selects top 20 important sentences to form the summary of the product, which is presented to the user. This work conducts several experiments to compare the effectiveness of TextRank, Luhn’s method, and the proposed approach. Among them, the approach proposed in this work exhibits the best performance.