Textually Summarising Incomplete Data

Stephanie Inglis, Ehud Reiter, Somayajulu Sripada
2017 Proceedings of the 10th International Conference on Natural Language Generation  
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.
doi:10.18653/v1/w17-3535 dblp:conf/inlg/InglisRS17 fatcat:rw3jmm2zefdg7ehcjdbdank3ya