Evaluating Neural Text Simplification in the Medical Domain

Laurens van den Bercken, Robert-Jan Sips, Christoph Lofi

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

45 Citations (Scopus)
1169 Downloads (Pure)


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 languageEnglish
Title of host publicationWWW'19 The World Wide Web Conference (WWW)
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages7
ISBN (Print)978-1-4503-6674-8/19/05
Publication statusPublished - May 2019
EventWWW 2019 : The Web Conference 2019, 30 years of the web - San Francisco, CA, United States
Duration: 13 May 201917 May 2019
Conference number: 30


ConferenceWWW 2019
Abbreviated titleWWW'19
Country/TerritoryUnited States
CitySan Francisco, CA


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


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