Evaluation Dataset for J apanese Medical Text Simplification (2024)

Abstract: We create a parallel corpus for medical text simplification in Japanese, which simplifies medical terms into expressions that patients can understand without effort.While text simplification in the medial domain is strongly desired by society, it is less explored in Japanese because of the lack of language resources.In this study, we build a parallel corpus for Japanese text simplification evaluation in the medical domain using patients’ weblogs.This corpus consists of 1,425 pairs of complex and simple sentences with or without medical terms.To tackle medical text simplification without a training corpus of the corresponding domain, we repurpose a Japanese text simplification model of other domains.Furthermore, we propose a lexically constrained reranking method that allows to avoid technical terms to be output.Experimental results show that our method contributes to achieving higher simplification performance in the medical domain.

## introduction medical texts contain a lot of technical terms which are often difficult to understand for laypeople @xcite . for better communication between medical practitioners and patients, medical text simplification has been actively studied in english so that the patients understand their diseases and symptoms and the courses of treatments in detail @xcite @xcite @xcite . however, such studies have not been well explored in japanese because of the lack of a parallel corpus of this domain. in the case of english, a typical approach is to employ pre-trained models in medical domain @xcite or models specialized for text simplification @xcite such as simplebert. however, there have not been any off-the-shelf pre-trained models of these kinds in japanese. as a step-forward to japanese medical text simplification, we create and release a parallel corpus for evaluation, named jasmine. 1 in addition, we tackle this problem without any domain-specific training corpus by developing a japanese version of simplebart, foot_1 a pre-trained model specialized for text simplification. furthermore, we propose a reranking method that avoids technical terms and employ it to the text simplification model. experimental results on our jasmine corpus confirm efficacy of out methods. ## jasmine corpus to enable the evaluation of medical text simplification in japanese, we create a parallel corpus, named jasmine 1 , consisting of sentences with and without medical terms. we employ patients' weblog articles written primarily for daily records of symptoms and treatments and communication with other patients foot_5 as medical texts for nonprofessional use (henceforth, it will be referred to simply as a blog). we construct a parallel corpus consisting of pairs of complex and simple sentences with or without technical terms by manually paraphrasing the (simple) blog sentences to use medical terms in meddra. 7 as shown in ## japanese simplification model for achieving medical text simplification in japanese, we develop a pre-trained model specific to the text simplification task (section 4.1) and propose a reranking method for avoiding technical terms (section 4.2). ## experiments to evaluate the performance of the medical text simplification, we experiment with the jasmine that we constructed. we also experiment with snow @xcite and jades @xcite , which are existing parallel corpora for japanese text simplification, to evaluate simplification performance in other domains. in jas-mine, we also evaluate the effectiveness of lexically constrained reranking with the meddra dictionary. 7 ## conclusion this study released a set of language resources for medical text simplification in japanese: an evaluation corpus of jasmine 1 and a pre-trained model of japanese simplebart. 2 experimental results in three domains, including medical, show that our japanese simplebart consistently achieves high performance, and reveal the effectiveness of pretraining specialized for text simplification. in the medical domain, our lexically constrained reranking to avoid technical terms further improved the simplification performance. our future work includes the construction of a large-scale training parallel corpus for medical text simplification in japanese. we also plan to examine more sophisticated lexical constraints, such as allowing common technical terms to be output.