Opinion Mining is concerned with the skillful extraction of vital information from opinionated text. Due to the rapid growth of social media sites, discussion forums, and online stores in the recent past, thousands of opinions are generated on hourly basis. Examining all these reviews from several sources is a dangling task. To grapple with this problem, opinion summarization is a way, where summary is generated from a set of opinionated data. Nevertheless, making an optimal opinion summarization system is a challenging task. This paper presents an overview of the approaches experimented and practiced so far in the field of Opinion Mining and Summarization and a survey of those techniques/approaches. These include: 1) Natural Language Processing and data mining techniques 2) Machine learning, deep learning and lexicon-based methods for sentiment prediction 3) Methods used for summarization. In a nutshell, an innovative framework is presented, which is an amalgam of different types of opinionated summarization techniques.