Evaluating Neural Text Simplification in the Medical Domain

Evaluating Neural Text Simplification in the Medical Domain

van den Bercken, Laurens (Student TU Delft; myTomorrows)
Sips, R.H.J. (myTomorrows)
Lofi, C. (TU Delft Web Information Systems)

2019-05

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.

Medical Text Simplification
Test and Training Data Generation
Monolingual Neural Machine Translation

http://resolver.tudelft.nl/uuid:a9d4cbd3-b7a8-49f1-81c6-174864aa8aa6

https://doi.org/10.1145/3308558.3313630

Association for Computing Machinery (ACM), New York

978-1-4503-6674-8/19/05

WWW'19 The World Wide Web Conference (WWW)

WWW 2019, 2019-05-13 → 2019-05-17, San Francisco, CA, United States

Institutional Repository

conference paper

© 2019 Laurens van den Bercken, R.H.J. Sips, C. Lofi