Customer feedback in the form of online reviews is an important source of information to manufacturers or service providers for evaluation of their products or services. Online reviews also help potential buyers in making their decisions. Manual checking of these huge amount of unstructured texts is time consuming. Several attempts have been made for opinion aggregation of online reviews but a generalized automated technique has yet to be developed. In this work, an efficient rule based technique for aspect wise summarization of online product reviews irrespective of their categories has been designed. The proposed technique develops the rules for extracting aspects and associating the opinion words to the respective aspects followed by effective grouping and summarization of aspect-opinion pairs into human interpretable form. The algorithm has been implemented on Amazon Product Reviews and evaluated against manually annotated ground truth. The result shows promising similarity with human judgement.