{"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/med-easi-finely-annotated-dataset-and-models","title":"Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts","arxiv_id":"2302.09155","date":"2023-02-17","proceeding":null,"authors":["Chandrayee Basu","Rosni Vasu","Michihiro Yasunaga","Qian Yang"],"abstract":"Automatic medical text simplification can assist providers with patient-friendly communication and make medical texts more accessible, thereby improving health literacy. But curating a quality corpus for this task requires the supervision of medical experts. In this work, we present $\\textbf{Med-EASi}$ ($\\underline{\\textbf{Med}}$ical dataset for $\\underline{\\textbf{E}}$laborative and $\\underline{\\textbf{A}}$bstractive $\\underline{\\textbf{Si}}$mplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical texts. Its $\\textit{expert-layman-AI collaborative}$ annotations facilitate $\\textit{controllability}$ over text simplification by marking four kinds of textual transformations: elaboration, replacement, deletion, and insertion. To learn medical text simplification, we fine-tune T5-large with four different styles of input-output combinations, leading to two control-free and two controllable versions of the model. We add two types of $\\textit{controllability}$ into text simplification, by using a multi-angle training approach: $\\textit{position-aware}$, which uses in-place annotated inputs and outputs, and $\\textit{position-agnostic}$, where the model only knows the contents to be edited, but not their positions. Our results show that our fine-grained annotations improve learning compared to the unannotated baseline. Furthermore, $\\textit{position-aware}$ control generates better simplification than the $\\textit{position-agnostic}$ one. The data and code are available at https://github.com/Chandrayee/CTRL-SIMP.","url_abs":"https://arxiv.org/abs/2302.09155v1","url_pdf":"https://arxiv.org/pdf/2302.09155v1.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":"med-easi-finely-annotated-dataset-and-models","repo_url":"https://github.com/chandrayee/ctrl-simp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":null,"task_name":"Position"},{"task_slug":"text-simplification","task_name":"Text Simplification"}],"methods":[],"datasets_introduced":[{"slug":"med-easi","name":"Med-EASi","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2302.09155","atlas_url":"https://app.syntology.ai/?focus=2302.09155","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.09155"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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