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    <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.</abstract>
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%0 Conference Proceedings
%T Textually Summarising Incomplete Data
%A Inglis, Stephanie
%A Reiter, Ehud
%A Sripada, Somayajulu
%Y Alonso, Jose M.
%Y Bugarín, Alberto
%Y Reiter, Ehud
%S Proceedings of the 10th International Conference on Natural Language Generation
%D 2017
%8 September
%I Association for Computational Linguistics
%C Santiago de Compostela, Spain
%F inglis-etal-2017-textually
%X 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.
%R 10.18653/v1/W17-3535
%U https://aclanthology.org/W17-3535
%U https://doi.org/10.18653/v1/W17-3535
%P 228-232

Markdown (Informal)

[Textually Summarising Incomplete Data](https://aclanthology.org/W17-3535) (Inglis et al., INLG 2017)

• Textually Summarising Incomplete Data (Inglis et al., INLG 2017)

ACL

• Stephanie Inglis, Ehud Reiter, and Somayajulu Sripada. 2017. Textually Summarising Incomplete Data. In Proceedings of the 10th International Conference on Natural Language Generation, pages 228–232, Santiago de Compostela, Spain. Association for Computational Linguistics.