Reading comprehension involves ability of reading the text, understanding the meaning of the given text passage and answering the questions based on it. It is a challenging task for machines, as it requires natural language understanding and knowledge about the world. In order to understand the meaning of the text, it is necessary to identify the context and organization of the given passage. It also involves ability to draw inference based on sentences in the paragraph. In this type of answering, reasoning is dependent on the type of comprehension dataset. The answering can be based on single sentence or set of multiple sentences. The complexity of the reasoning required in reading comprehension dataset depends on several factors such as source of paragraphs, the ordering of sentences in the paragraphs and type of questions. Answering the questions based on context requires identification of linguistic features, based on syntax, semantics, and different sentence patterns appearing in the paragraph text. This work presents, exhaustive study of the various approaches used by different authors for feature extraction in the domain of reading comprehension. Most of the recent work in reading comprehension is mainly focused on application of deep learning algorithms but it may not work well with low resource data. Low resource datasets having diverse linguistic features, need deep understanding of the text. At the end, we discuss some of the open challenges in modeling the comprehension systems.