Bug Reports are one of the very important artifacts during software development process and is one of the very popular artifacts of research among the researchers. Summarization is one application on bug reports which helps solve a lot of interesting issues of bug reports like bug triaging and bug duplicate detection. Many researchers have done research on bug report summarization using various techniques like supervised approaches, unsupervised approaches, deep learning approach, feature-based approach. In this paper, we have systematically evaluated the works and presented them in the comparative form. For our comparison work, we have selected five research papers among all. The papers are chosen with the thing in mind that all the important concepts which are getting used for bug report summarization gets covered. The paper discusses the approach, concept, strengths, limitations, tools if used, dataset used, the evaluation techniques and the performance results that are used or obtained in the chosen research works. Our work will help other researchers have a clear overview of the very popular works in this field and thus will help improve and carry out further works in this field of research.