| Autor: |
Moramarco, Francesco, Juric, Damir, Savkov, Aleksandar, Flann, Jack, Lehl, Maria, Boda, Kristian, Grafen, Tessa, Zhelezniak, Vitalii, Gohil, Sunir, Korfiatis, Alex Papadopoulos, Hammerla, Nils |
| Rok vydání: |
2021 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| Popis: |
Clinical notes are an efficient way to record patient information but are notoriously hard to decipher for non-experts. Automatically simplifying medical text can empower patients with valuable information about their health, while saving clinicians time. We present a novel approach to automated simplification of medical text based on word frequencies and language modelling, grounded on medical ontologies enriched with layman terms. We release a new dataset of pairs of publicly available medical sentences and a version of them simplified by clinicians. Also, we define a novel text simplification metric and evaluation framework, which we use to conduct a large-scale human evaluation of our method against the state of the art. Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning, surpassing the current state of the art. |
| Databáze: |
arXiv |
| Externí odkaz: |
|
