Abstract
Many data-to-text NLG systems work with data sets which are incomplete, ie some of the data is missing. We have worked with data journalists to understand how they describe incomplete data, and are building NLG algorithms based on these insights. A pilot evaluation showed mixed results, and highlighted several areas where we need to improve our system.
Cite
CITATION STYLE
Inglis, S., Reiter, E., & Sripada, S. (2017). Textually summarising incomplete data. In INLG 2017 - 10th International Natural Language Generation Conference, Proceedings of the Conference (pp. 228–232). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-3535
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