  The task of fact-checking deals with assessing the veracity of factual claims
based on credible evidence and background knowledge. In particular, scientific
fact-checking is the variation of the task concerned with verifying claims
rooted in scientific knowledge. This task has received significant attention
due to the growing importance of scientific and health discussions on online
platforms. Automated scientific fact-checking methods based on NLP can help
combat the spread of misinformation, assist researchers in knowledge discovery,
and help individuals understand new scientific breakthroughs. In this paper, we
present a comprehensive survey of existing research in this emerging field and
its related tasks. We provide a task description, discuss the construction
process of existing datasets, and analyze proposed models and approaches. Based
on our findings, we identify intriguing challenges and outline potential future
directions to advance the field.
