Describing Complex Charts in Natural Language: A Caption Generation System
Describing Complex Charts in Natural Language: A Caption Generation System
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发表时间：
1998-09
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通讯作者：
Vibhu Mittal;Johanna D. Moore;G. Carenini;Steven F. Roth
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作者：
Vibhu Mittal;Johanna D. Moore;G. Carenini;Steven F. Roth
图形表示可用于简洁有效地传达关系数据集中的信息。然而，表示许多属性和关系的新颖图形表示在得到解释之前通常很难完全理解。因此，自动生成的图形表示必须要么仅限于生成简单的、常规的图形表示，要么面临难以理解的风险。该问题的一个可能的解决方案是扩展自动图形呈现系统以生成自然语言的解释性标题，以使用户能够理解图形中表达的信息。本文提出了一个这样做的系统。它使用文本规划器来确定标题的内容和结构，基于：（1）图形表示结构的表示及其到所描述的数据的映射，（2）用于识别图形元素的感知复杂性的框架，以及（3）图形中表达的数据的结构。规划器的输出进一步处理有关排序、聚合、居中、生成引用表达式和词汇选择等问题。我们讨论我们系统的架构及其优点和局限性。我们的实现目前仅限于二维图表和地图，但是，除了词汇信息之外，它是完全独立于领域的。我们用有关匹兹堡房屋销售的数据和生成的标题来说明我们的讨论。
Graphical presentations can be used to communicate information in relational data sets succinctly and effectively. However, novel graphical presentations that represent many attributes and relationships are often difficult to understand completely until explained. Automatically generated graphical presentations must therefore either be limited to generating simple, conventionalized graphical presentations, or risk incomprehensibility. A possible solution to this problem would be to extend automatic graphical presentation systems to generate explanatory captions in natural language, to enable users to understand the information expressed in the graphic. This paper presents a system to do so. It uses a text planner to determine the content and structure of the captions based on: (1) a representation of the structure of the graphical presentation and its mapping to the data it depicts, (2) a framework for identifying the perceptual complexity of graphical elements, and (3) the structure of the data expressed in the graphic. The output of the planner is further processed regarding issues such as ordering, aggregation, centering, generating referring expressions, and lexical choice. We discuss the architecture of our system and its strengths and limitations. Our implementation is currently limited to 2-D charts and maps, but, except for lexical information, it is completely domain independent. We illustrate our discussion with figures and generated captions about housing sales in Pittsburgh.