  Table-and-text hybrid question answering (HybridQA) is a widely used and
challenging NLP task commonly applied in the financial and scientific domain.
The early research focuses on migrating other QA task methods to HybridQA,
while with further research, more and more HybridQA-specific methods have been
present. With the rapid development of HybridQA, the systematic survey is still
under-explored to summarize the main techniques and advance further research.
So we present this work to summarize the current HybridQA benchmarks and
methods, then analyze the challenges and future directions of this task. The
contributions of this paper can be summarized in three folds: (1) first survey,
to our best knowledge, including benchmarks, methods and challenges for
HybridQA; (2) systematic investigation with the reasonable comparison of the
existing systems to articulate their advantages and shortcomings; (3) detailed
analysis of challenges in four important dimensions to shed light on future
directions.
