  The usage and amount of information available on the internet increase over
the past decade. This digitization leads to the need for automated answering
system to extract fruitful information from redundant and transitional
knowledge sources. Such systems are designed to cater the most prominent answer
from this giant knowledge source to the user query using natural language
understanding (NLU) and thus eminently depends on the Question-answering(QA)
field.
  Question answering involves but not limited to the steps like mapping of user
question to pertinent query, retrieval of relevant information, finding the
best suitable answer from the retrieved information etc. The current
improvement of deep learning models evince compelling performance improvement
in all these tasks.
  In this review work, the research directions of QA field are analyzed based
on the type of question, answer type, source of evidence-answer, and modeling
approach. This detailing followed by open challenges of the field like
automatic question generation, similarity detection and, low resource
availability for a language. In the end, a survey of available datasets and
evaluation measures is presented.
