{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/patient-friendly-clinical-notes-towards-a-new","title":"Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset","arxiv_id":null,"date":"2022-12-07","proceeding":"Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR) 2022 12","authors":["Jan Trienes","Jörg Schlötterer","Hans-Ulrich Schildhaus","Christin Seifert"],"abstract":"Automatic text simplification can help patients to better understand their own clinical notes. A major hurdle for the development of clinical text simplification methods is the lack of high quality resources. We report ongoing efforts in creating a parallel dataset of professionally simplified clinical notes. Currently, this corpus consists of 851 document-level simplifications of German pathology reports. We highlight characteristics of this dataset and establish first baselines for paragraph-level simplification.","url_abs":"https://aclanthology.org/2022.tsar-1.3/","url_pdf":"https://aclanthology.org/2022.tsar-1.3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"patient-friendly-clinical-notes-towards-a-new","repo_url":"https://github.com/jantrienes/simple-patho","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"text-simplification","task_name":"Text Simplification"}],"methods":[{"method_slug":"mbart","method_name":"mBART"},{"method_slug":"mbert","method_name":"mBERT"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}