@inproceedings{thomson-etal-2023-enhancing,
title = "Enhancing factualness and controllability of Data-to-Text Generation via data Views and constraints",
author = "Thomson, Craig and
Rebuffel, Clement and
Reiter, Ehud and
Soulier, Laure and
Sripada, Somayajulu and
Gallinari, Patrick",
editor = "Keet, C. Maria and
Lee, Hung-Yi and
Zarrie{\ss}, Sina",
booktitle = "Proceedings of the 16th International Natural Language Generation Conference",
month = sep,
year = "2023",
address = "Prague, Czechia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.inlg-main.16",
doi = "10.18653/v1/2023.inlg-main.16",
pages = "221--236",
abstract = "Neural data-to-text systems lack the control and factual accuracy required to generate useful and insightful summaries of multidimensional data. We propose a solution in the form of data views, where each view describes an entity and its attributes along specific dimensions. A sequence of views can then be used as a high-level schema for document planning, with the neural model handling the complexities of micro-planning and surface realization. We show that our view-based system retains factual accuracy while offering high-level control of output that can be tailored based on user preference or other norms within the domain.",
}
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<abstract>Neural data-to-text systems lack the control and factual accuracy required to generate useful and insightful summaries of multidimensional data. We propose a solution in the form of data views, where each view describes an entity and its attributes along specific dimensions. A sequence of views can then be used as a high-level schema for document planning, with the neural model handling the complexities of micro-planning and surface realization. We show that our view-based system retains factual accuracy while offering high-level control of output that can be tailored based on user preference or other norms within the domain.</abstract>
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%0 Conference Proceedings
%T Enhancing factualness and controllability of Data-to-Text Generation via data Views and constraints
%A Thomson, Craig
%A Rebuffel, Clement
%A Reiter, Ehud
%A Soulier, Laure
%A Sripada, Somayajulu
%A Gallinari, Patrick
%Y Keet, C. Maria
%Y Lee, Hung-Yi
%Y Zarrieß, Sina
%S Proceedings of the 16th International Natural Language Generation Conference
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F thomson-etal-2023-enhancing
%X Neural data-to-text systems lack the control and factual accuracy required to generate useful and insightful summaries of multidimensional data. We propose a solution in the form of data views, where each view describes an entity and its attributes along specific dimensions. A sequence of views can then be used as a high-level schema for document planning, with the neural model handling the complexities of micro-planning and surface realization. We show that our view-based system retains factual accuracy while offering high-level control of output that can be tailored based on user preference or other norms within the domain.
%R 10.18653/v1/2023.inlg-main.16
%U https://aclanthology.org/2023.inlg-main.16
%U https://doi.org/10.18653/v1/2023.inlg-main.16
%P 221-236
Markdown (Informal)
[Enhancing factualness and controllability of Data-to-Text Generation via data Views and constraints](https://aclanthology.org/2023.inlg-main.16) (Thomson et al., INLG-SIGDIAL 2023)
ACL