  Objectives: An SLR is presented focusing on text mining based automation of
SLR creation. The present review identifies the objectives of the automation
studies and the aspects of those steps that were automated. In so doing, the
various ML techniques used, challenges, limitations and scope of further
research are explained.
  Methods: Accessible published literature studies that primarily focus on
automation of study selection, study quality assessment, data extraction and
data synthesis portions of SLR. Twenty-nine studies were analyzed.
  Results: This review identifies the objectives of the automation studies,
steps within the study selection, study quality assessment, data extraction and
data synthesis portions that were automated, the various ML techniques used,
challenges, limitations and scope of further research.
  Discussion: We describe uses of NLP/TM techniques to support increased
automation of systematic literature reviews. This area has attracted increase
attention in the last decade due to significant gaps in the applicability of TM
to automate steps in the SLR process. There are significant gaps in the
application of TM and related automation techniques in the areas of data
extraction, monitoring, quality assessment and data synthesis. There is thus a
need for continued progress in this area, and this is expected to ultimately
significantly facilitate the construction of systematic literature reviews.
