When making an online purchase, it becomes important for the customer to read the product reviews efficiently and make a decision quickly. However, reviews can be lengthy, contain repeated, or sometimes irrelevant information that does not help in decision making. In this paper, we introduce MRCBert, a novel unsupervised method to generate summaries. We leverage MRC approach to extract relevant opinions and generate both rating-wise and aspect-wise summaries from reviews. We also showed that MRCBert does not require domain-specific dataset for training and can also work with pre-trained summarization models that are not for opinion mining tasks, therefore it is scalable and transferable.