@inproceedings{sakakini-etal-2020-context,
title = "Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains",
author = "Sakakini, Tarek and
Lee, Jong Yoon and
Duri, Aditya and
Azevedo, Renato F.L. and
Sadauskas, Victor and
Gu, Kuangxiao and
Bhat, Suma and
Morrow, Dan and
Graumlich, James and
Walayat, Saqib and
Hasegawa-Johnson, Mark and
Huang, Thomas and
Willemsen-Dunlap, Ann and
Halpin, Donald",
editor = "Holderness, Eben and
Jimeno Yepes, Antonio and
Lavelli, Alberto and
Minard, Anne-Lyse and
Pustejovsky, James and
Rinaldi, Fabio",
booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.louhi-1.13/",
doi = "10.18653/v1/2020.louhi-1.13",
pages = "115--126",
abstract = "Healthcare systems have increased patients' exposure to their own health materials to enhance patients' health levels, but this has been impeded by patients' lack of understanding of their health material. We address potential barriers to their comprehension by developing a context-aware text simplification system for health material. Given the scarcity of annotated parallel corpora in healthcare domains, we design our system to be independent of a parallel corpus, complementing the availability of data-driven neural methods when such corpora are available. Our system compensates for the lack of direct supervision using a biomedical lexical database: Unified Medical Language System (UMLS). Compared to a competitive prior approach that uses a tool for identifying biomedical concepts and a consumer-directed vocabulary list, we empirically show the enhanced accuracy of our system due to improved handling of ambiguous terms. We also show the enhanced accuracy of our system over directly-supervised neural methods in this low-resource setting. Finally, we show the direct impact of our system on laypeople`s comprehension of health material via a human subjects' study (n=160)."
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<abstract>Healthcare systems have increased patients’ exposure to their own health materials to enhance patients’ health levels, but this has been impeded by patients’ lack of understanding of their health material. We address potential barriers to their comprehension by developing a context-aware text simplification system for health material. Given the scarcity of annotated parallel corpora in healthcare domains, we design our system to be independent of a parallel corpus, complementing the availability of data-driven neural methods when such corpora are available. Our system compensates for the lack of direct supervision using a biomedical lexical database: Unified Medical Language System (UMLS). Compared to a competitive prior approach that uses a tool for identifying biomedical concepts and a consumer-directed vocabulary list, we empirically show the enhanced accuracy of our system due to improved handling of ambiguous terms. We also show the enhanced accuracy of our system over directly-supervised neural methods in this low-resource setting. Finally, we show the direct impact of our system on laypeople‘s comprehension of health material via a human subjects’ study (n=160).</abstract>
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%0 Conference Proceedings
%T Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains
%A Sakakini, Tarek
%A Lee, Jong Yoon
%A Duri, Aditya
%A Azevedo, Renato F.L.
%A Sadauskas, Victor
%A Gu, Kuangxiao
%A Bhat, Suma
%A Morrow, Dan
%A Graumlich, James
%A Walayat, Saqib
%A Hasegawa-Johnson, Mark
%A Huang, Thomas
%A Willemsen-Dunlap, Ann
%A Halpin, Donald
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F sakakini-etal-2020-context
%X Healthcare systems have increased patients’ exposure to their own health materials to enhance patients’ health levels, but this has been impeded by patients’ lack of understanding of their health material. We address potential barriers to their comprehension by developing a context-aware text simplification system for health material. Given the scarcity of annotated parallel corpora in healthcare domains, we design our system to be independent of a parallel corpus, complementing the availability of data-driven neural methods when such corpora are available. Our system compensates for the lack of direct supervision using a biomedical lexical database: Unified Medical Language System (UMLS). Compared to a competitive prior approach that uses a tool for identifying biomedical concepts and a consumer-directed vocabulary list, we empirically show the enhanced accuracy of our system due to improved handling of ambiguous terms. We also show the enhanced accuracy of our system over directly-supervised neural methods in this low-resource setting. Finally, we show the direct impact of our system on laypeople‘s comprehension of health material via a human subjects’ study (n=160).
%R 10.18653/v1/2020.louhi-1.13
%U https://aclanthology.org/2020.louhi-1.13/
%U https://doi.org/10.18653/v1/2020.louhi-1.13
%P 115-126
Markdown (Informal)
[Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains](https://aclanthology.org/2020.louhi-1.13/) (Sakakini et al., Louhi 2020)
ACL
- Tarek Sakakini, Jong Yoon Lee, Aditya Duri, Renato F.L. Azevedo, Victor Sadauskas, Kuangxiao Gu, Suma Bhat, Dan Morrow, James Graumlich, Saqib Walayat, Mark Hasegawa-Johnson, Thomas Huang, Ann Willemsen-Dunlap, and Donald Halpin. 2020. Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains. In Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis, pages 115–126, Online. Association for Computational Linguistics.