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.

翻译：表格和文本混合回答问题(HybridQA)是一项广泛使用和具有挑战性的NLP任务,通常在财政和科学领域应用。早期研究的重点是将其他质量保证任务方法迁移到混合QA,同时随着进一步的研究,出现了越来越多的混合QA特定方法。随着混合QA的迅速发展,系统调查仍未得到充分探讨,以总结主要技术和推进进一步的研究。因此,我们介绍这项工作,总结当前混合QA的基准和方法,然后分析这项任务的挑战和未来方向。本文件的贡献可以分为三个部分:(1) 首次调查,了解我们的最佳知识,包括混合QA的基准、方法和挑战;(2) 系统调查,对现有系统系统系统系统进行合理的比较,以阐明其优点和缺点;(3) 详细分析四个重要方面的挑战,以说明今后的方向。