Content Selection in Data-to-Text Systems: A Survey

Abstract

Data-to-text systems are powerful in generating reports from data automatically and thus they simplify the presentation of complex data. Rather than presenting data using visualisation techniques, datato-text systems use natural (human) language, which is the most common way for human-human communication. In addition, data-to-text systems can adapt their output content to users' preferences, background or interests and therefore they can be pleasant for users to interact with. Content selection is an important part of every data-to-text system, because it is the module that determines which from the available information should be conveyed to the user. This survey initially introduces the field of data-to-text generation, describes the general data-to-text system architecture and then it reviews the state-ofthe-art content selection methods. Finally, it provides recommendations for choosing an approach and discusses opportunities for future research.

Neural Data-to-Text Generation with LM-based Text Augmentation

“…(van der Lee et al, 2018). This makes using neural systems less appealing: oftentimes, in-domain text samples are not readily available, and there is a high cost to collecting in-domain texts which fit the data samples, and annotating these texts with the data labels -the cost for collecting this data might hence even outweigh the efforts of designing a rule-based system (Gkatzia, 2016). The goal of this work is to improve the performance of neural data-to-text models in scenarios where only very few text samples exist (we assume that these text samples are paired with corresponding data samples).…”

DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool

“…Neural data-to-text generation has been the subject of much research in recent years (Gkatzia, 2016). Traditionally, the task takes as input structured data which comes in the form of tables with attribute and value pairs, and generates free-form, human-readable text.…”