An acronym is a textual form used to refer an entity and to stress the important concepts. Over the last two decades, many researchers worked for mining acronym expansion pairs from plain text and Web. This is mainly used in language processing, information retrieval, Web search, ontology mapping, question answering, SMS, and social media posting. Acronyms are dynamically growing day by day, and discovering its definition/expansion is becoming a challenging task because of its diversified characteristics. Manually edited online repositories have acronym definition pairs, but it is an overwhelming task to update all possible definitions systematically. To extend the support, different approaches are employed for the automatic detection of acronym definitions from text and Web documents. This paper presents those approaches and also reveals the Web-based methods used for disambiguating, ranking, finding popularity score, and context words of the expansions. The scope for the future work in this research area is also conferred in this paper.