M ulti MSD : A Corpus for Multilingual Medical Text Simplification from Online Medical References (2025)

Abstract: We 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. However, 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 10,000 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.

## introduction medical texts contain many technical terms (medical terms), and non-expert patients often cannot use the information effectively @xcite . also, healthcare professionals frequently use technical terms when communicating with patients, even though they recognize that they should avoid them @xcite . 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 are easily understood by patients is expected. in this background, while medical text simplification has been actively researched in english @xcite @xcite @xcite , it has not been sufficiently studied in non-english languages. in 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 @xcite . in english, medical text simplification models are trained using large-scale parallel corpus for training @xcite . 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 @xcite . for spanish and french as well, only small-scale medical text simplification parallel corpora exist @xcite @xcite , which are not sufficient for training text simplification models. therefore, a large-scale parallel corpus for medical text simplification is desired. we 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 article pairs to automatically construct multilingual parallel corpora for text simplification, multimsd corpus. we then train medical text simplification models using our corpus and evaluate their perfor-reference sentence pairs language automets @xcite 3,300 english - @xcite 4,554 english msd @xcite 930 english medlane @xcite 14,832 english med-easi @xcite 1,979 english plaba @xcite 7,643 english cochrane-auto @xcite 35,800 english clara-med @xcite 3,800 spanish clear @xcite 4,596 french jasmine @xcite 1,425 japanese as in our study, @xcite determined that the msd manual, online medical references, is a useful language resource in medical text simplification. they have constructed a parallel corpus by collecting professional and consumer article pairs from the msd manual and manually aligning them by experts. however, while this method provides high-quality alignment, it is costly and limited in scale. furthermore, they use only english articles and do not focus on other languages. in this study, we collect professional and consumer article pairs from the msd manual across nine languages and conduct embeddingbased sentence alignment to construct a medical text simplification parallel corpus with a scale of 10,000 sentence pairs in each language. ## multimsd corpus we focus on the msd manual 1 of the online medical reference to construct a large-scale parallel corpus. msd manual is the world's most widely used source of medical information on all medical topics, with a professional version written for specialists such as doctors and healthcare workers, and a consumer version written for the general public en deep venous thrombosis (dvt) is clotting of blood in a deep vein of an extremity (usually calf or thigh) or the pelvis. deep vein thrombosis is the formation of blood clots (thrombi) in the deep veins, usually in the legs. fr la thrombose veineuse profonde correspond à la formation d'un caillot sanguin dans une veine profonde d'un membre (habituellement le mollet ou les cuisses) ou le petit bassin. la thrombose veineuse profonde est la formation de caillots sanguins (thrombi) à l'intérieur des veines profondes, généralement dans les jambes. it la trombosi venosa profonda consiste nella formazione di un coagulo di sangue in una vena profonda di un arto (solitamente a livello del polpaccio o della coscia) o della pelvi. la trombosi venosa profonda consiste nella formazione di coaguli di sangue (trombi) all'interno delle vene profonde, in genere delle gambe. ja 深部静脈血栓症(dvt)とは,四肢(通常は腓腹部または大腿部)または骨盤の深部静脈で血液が凝固する病態である。 深部静脈血栓症は、深部静脈に血栓(血液のかたまり)が形成される病気で、通常は脚で発生します。 ## proposed method the conservative behavior of text simplification models is one of their traditional challenges (kajiwara, 2019). to address this problem, we proposed a method to actively paraphrase terminology in this section. we propose a method that weights the crossentropy loss of the correct words in the reference sentence not included in the input sentence to promote the paraphrasing of medical terms and complex words in the input sentence that are written for experts. when the loss of a word is increased, the system is trained to actively output that word, so that more simple words contained in reference sentences written for the general public are expected to be output. in this method, if the correct word w in the reference sentence is not included in the input sentence x, its cross-entropy loss l(w) is multiplied by the weight θ. finally, the word loss l ′ (w) is as follows. ## experiments through experiments on text simplification in the medical domain, we validate the usefulness of the multimsd corpus constructed in this study. ## analysis 6.1 examples of medical text simplification ## conclusion in this study, we automatically constructed parallel corpora for training consisting of nine languages by embedding-based sentence alignment from professional and consumer article pairs in the online medical references to facilitate research on medical text simplification in non-english. we compared 4 types of multilingual sentence embeddings: mbert, xlm-r, labse, and me5. we found that me5 was useful for sentence alignment in english medical texts, while labse was useful for japanese. evaluating the performance of the text simplification models using the corpus constructed in this study, we found that the large language model llama outperformed mbart, which was pre-trained on multilingual data. furthermore, we confirmed that the proposed method of weights the loss of correct words in reference sentences not included in input sentences improves the performance of medical text simplification.