Textually Summarising Incomplete Data

Stephanie Inglis, Ehud Reiter, Somayajulu Sripada


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.
Anthology ID:
W17-3535
Volume:
Proceedings of the 10th International Conference on Natural Language Generation
Month:
September
Year:
2017
Address:
Santiago de Compostela, Spain
Venues:
INLG | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
228–232
Language:
URL:
https://www.aclweb.org/anthology/W17-3535
DOI:
10.18653/v1/W17-3535
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-3535.pdf