[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-640666-105":59,"doc-detail-640666-en":134},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":127,"head_meta":129,"extra_data":131,"updated_unix":133},105,"en","multimsd-a-corpus-for-multilingual-medical-text-simplification-from-online-medical-references","MultiMSD - A Corpus for Multilingual Medical Text Simplification from Online Medical References","","We release a parallel corpus for medical text simplification that rephrases medical terms into patient-friendly expressions. Medical writing by practitioners contains many technical terms, limiting usefulness for non-expert patients, and creating demand for simplification without medical terminology. The work addresses limited research in non-English settings by building nine-language parallel corpora from online medical references with automatic sentence alignment. It also proposes model training to actively paraphrase complex medical terms, improving simplification performance, and shows multilingual training outperforms monolingual training.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/multimsd-a-corpus-for-multilingual-medical-text-simplification-from-online-medical-references/640666/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/multimsd-a-corpus-for-multilingual-medical-text-simplification-from-online-medical-references/640666.png","ImageObject",300,407,{"name":92,"@type":93},"Dusta123","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-24","2026-10-22",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does MultiMSD address in medical text simplification?","Question",{"text":112,"@type":113},"It targets the difficulty non-expert patients face when medical practitioner texts contain many technical terms, limiting their ability to use the information effectively.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the MultiMSD parallel corpus constructed?",{"text":117,"@type":113},"It was built using online medical references from MSD manual articles and embedding-based sentence alignment between professional and layperson article pairs.",{"name":119,"@type":110,"acceptedAnswer":120},"Which languages are included in the released parallel corpora?",{"text":121,"@type":113},"German, English, Spanish, French, Italian, Japanese, Portuguese, Russian, and Chinese are included, with 10,000 sentence pairs per language.",{"name":123,"@type":110,"acceptedAnswer":124},"What do the experimental results indicate about model training?",{"text":125,"@type":113},"The proposed training method improves medical text simplification performance, and training with a multilingual dataset is more effective than training with a monolingual dataset.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},640666,1792811299,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149},962093541925,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","MultiMSD: A Corpus for Multilingual Medical Text Simplification from Online Medical References  \nKoki Horiguchi† Tomoyuki Kajiwara† Takashi Ninomiya† Shoko Wakamiya‡ Eiji Aramaki‡† Ehime University ‡ Nara Institute of Science and Technology  \n{horiguchi@ai.cs . , kajiwara@cs. , [ninomiya.takashi.mk@}ehime-u.ac.jp](ninomiya.takashi.mk@}ehime-u.ac.jp)  \n{wakamiya, [aramaki}@is.naist.jp](aramaki}@is.naist.jp)  \nAbstract  \nWe release a parallel corpus for medical text simplification, which paraphrases medical terms into expressions easily understood by patients. Medical texts written by medical practitioners contain a lot of technical terms, and patients who are non-experts are often unable to use the information effectively. Therefore, there is a strong social demand for medical text simplification that paraphrases input sentences without using medical terms.  \nHowever, this task has not been sufficiently studied in non-English languages. We therefore developed parallel corpora for medical text simplification in nine languages: German, English, Spanish, French, Italian, Japanese, Portuguese, Russian, and Chinese, each with  \n10 , 000 sentence pairs, by automatic sentence alignment to online medical references for professionals and consumers. We also propose a method for training text simplification models to actively paraphrase complex expressions, including medical terms. Experimental results show that the proposed method improves the performance of medical text simplification. In addition, we confirmed that training with a multilingual dataset is more effective than training with a monolingual dataset.  \n1 Introduction  \nMedical texts contain many technical terms (medical terms), and non-expert patients often cannot use the information effectively (Cheng and Dunn, 2015) . Also, healthcare professionals frequently use technical terms when communicating with patients, even though they recognize that they should avoid them (Charpentier et al., 2021) . Therefore, to facilitate patients’ understanding of medical conditions and treatment methods, and to assist medical practitioners in explaining important medical information such as findings and diagnoses to patients, medical text simplification that paraphrases medical terms into expressions that  \nare easily understood by patients is expected. In this background, while medical text simplification has been actively researched in English (Cao et al., 2020 ; Sakakini et al., 2020 ; Devaraj et al., 2021 ; Guo et al., 2021 ; Luo et al., 2022), it has not been sufficiently studied in non-English languages.  \nIn medical text simplification, a parallel corpus consisting of pairs of complex sentences written for experts and simple sentences written for general audiences are used to train seq2seq models such as Transformer (Vaswani et al., 2017) . In English, medical text simplification models are trained using large-scale parallel corpus for training (Luo et al., 2022 ; Bakker and Kamps, 2024) . On the other hand, in Japanese, where only smallscale evaluation corpora for medical text simplification exist, text simplification models trained on other domains have been applied to medical text simplification (Horiguchi et al., 2024) . For Spanish and French as well, only small-scale medical text simplification parallel corpora exist (Grabar and Cardon, 2018 ; Cardon and Grabar, 2020 ; Campillos-Llanos et al., 2022), which are not sufficient for training text simplification models. Therefore, a large-scale parallel corpus for medical text simplification is desired.  \nWe utilize the online medical references of the MSD manual 1 to construct a large-scale parallel corpus for medical text simplification. This online medical references contains articles written for professionals such as medical practitioners, and articles written for the laypeople such as patients and their families, each of which is available in nine languages. Therefore, we performed embedding-based sentence alignment for those ar","cbCaiokDffwx1Id7","https://ap.wps.com/l/cbCaiokDffwx1Id7","pdf",388939,11,"English","# Abstract\n# 1 Introduction\n# 2 Related Work","[{\"question\":\"What problem does MultiMSD address in medical text simplification?\",\"answer\":\"It targets the difficulty non-expert patients face when medical practitioner texts contain many technical terms, limiting their ability to use the information effectively.\"},{\"question\":\"How was the MultiMSD parallel corpus constructed?\",\"answer\":\"It was built using online medical references from MSD manual articles and embedding-based sentence alignment between professional and layperson article pairs.\"},{\"question\":\"Which languages are included in the released parallel corpora?\",\"answer\":\"German, English, Spanish, French, Italian, Japanese, Portuguese, Russian, and Chinese are included, with 10,000 sentence pairs per language.\"},{\"question\":\"What do the experimental results indicate about model training?\",\"answer\":\"The proposed training method improves medical text simplification performance, and training with a multilingual dataset is more effective than training with a monolingual dataset.\"}]","MultiMSD - A Corpus for Multilingual Medical Text Simplification from Online Medical References | PDF",1792710222,28]