Abstract
Health literacy, i.e. the ability to read and understand medical text, is a relevant component of public health. Unfortunately, many medical texts are hard to grasp by the general population as they are targeted at highly-skilled professionals and use complex language and domain-specific terms. Here, automatic text simplification making text commonly understandable would be very beneficial. However, research and development into medical text simplification is hindered by the lack of openly available training and test corpora which contain complex medical sentences and their aligned simplified versions. In this paper, we introduce such a dataset to aid medical text simplification research. The dataset is created by filtering aligned health sentences using expert knowledge from an existing aligned corpus and a novel simple, language independent monolingual text alignment method. Furthermore, we use the dataset to train a state-of-the-art neural machine translation model, and compare it to a model trained on a general simplification dataset using an automatic evaluation, and an extensive human-expert evaluation.
Original language | English |
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Title of host publication | WWW'19 The World Wide Web Conference (WWW) |
Place of Publication | New York |
Publisher | Association for Computing Machinery (ACM) |
Pages | 3286-3292 |
Number of pages | 7 |
ISBN (Print) | 978-1-4503-6674-8/19/05 |
DOIs | |
Publication status | Published - May 2019 |
Event | WWW 2019 : The Web Conference 2019, 30 years of the web - San Francisco, CA, United States Duration: 13 May 2019 → 17 May 2019 Conference number: 30 |
Conference
Conference | WWW 2019 |
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Abbreviated title | WWW'19 |
Country/Territory | United States |
City | San Francisco, CA |
Period | 13/05/19 → 17/05/19 |
Keywords
- Medical Text Simplification
- Test and Training Data Generation
- Monolingual Neural Machine Translation