Several studies have suggested that comments describing the code can help mitigate the burden of program understanding. However, software systems usually lack adequate comments and even when present, the comments may be obsolete or unhelpful. Researchers have addressed this issue by automatically generating comments from source code, a task referred to as Code Summarization. In this technical presentation, we take a deeper look at some of the significant, recent works in the area of code summarization and how each of them attempts to take a new perspective of this task including methods leveraging RNNs, Transformers, Graph representation learning and Reinforcement learning. We present a background of the techniques involved and how they are leveraged to solve the problem of code summarization. We review individual methods in detail, highlight their strengths and weaknesses and discuss future avenues for this task.