Tables are probably the most natural way to represent relational data in various media and formats. They store a large number of valuable facts that could be utilized for question answering, knowledge base population, natural language generation, and other applications. However, many tables are not accompanied by semantics for the automatic interpretation of the information they present. Table Understanding (TU) aims at recovering the missing semantics that enables the extraction of facts from tables. This problem covers a range of issues from table detection in document images to semantic table interpretation with the help of external knowledge bases. To date, the TU research has been ongoing on for 30 years. Nevertheless, there is no common point of view on the scope of TU; the terminology still needs agreement and unification. In recent years, science and technology have shown a rapidly increasing interest in TU. Nowadays, it is especially important to check the meaning of this research problem once again. This article gives a comprehensive characterization of the TU problem, including a description of its subproblems, tasks, subtasks, and applications. It also discusses the common limitations used in the existing problem statements and proposes some directions for further research that would help overcome the corresponding limitations.

中文翻译：

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表格理解：问题概述

表格可能是在各种媒体和格式中表示关系数据的最自然方式。它们存储了大量有价值的事实，可用于问答、知识库填充、自然语言生成和其他应用。然而，许多表格并没有自动解释它们呈现的信息的语义。表格理解(TU) 旨在恢复缺失的语义，以便从表中提取事实。这个问题涵盖了从文档图像中的表格检测到借助外部知识库进行语义表格解释的一系列问题。迄今为止，TU的研究已经进行了30年。然而，关于 TU 的范围没有共同的观点；术语仍然需要一致和统一。近年来，科学技术对 TU 表现出快速增长的兴趣。如今，再次检验这个研究问题的意义显得尤为重要。本文给出了 TU 问题的全面表征，包括对其子问题、任务、子任务和应用程序的描述。