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Register a new userParagraph-level Simplification of Medical Texts
تبسيط مستوى الفقرة للنصوص الطبية
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نحن نعتبر مشكلة التعلم بتبسيط النصوص الطبية. هذا مهم لأن معظم المعلومات الأكثر موثوقية وحديثة في الطب الحيوي كثيفة مع Jargon وبالتالي لا يمكن الوصول إليها عمليا للجمهور العادي. علاوة على ذلك، لا يتجاوز التبسيط اليدوي للجسم بسرعة متزايدة من الأدب الطبي الطبيعي، يحفز الحاجة إلى النهج الآلي. لسوء الحظ، لا توجد موارد واسعة النطاق المتاحة لهذه المهمة. في هذا العمل، نقدم جثة جديدة من النصوص الموازية باللغة الإنجليزية تضم ملخصات تقنية ووضع جميع الأدلة المنشورة المتعلقة بالموضوعات السريرية المختلفة. بعد ذلك، نقترح مقياسا جديدا يستند إلى درجات احتمالية من نموذج لغة ملثم مسبقا على النصوص العلمية. نظهر أن هذا التدبير الآلي يتحمل أفضل بين الملخصات التقنية والوضعية من الاستدلال القائمة. نقدم وتقييم نماذج محول ترميز تشفير الأساس لتبسيطها واقتراح تكبير رواية لهذه التي تعاقب فيها بشكل صريح فك الترميز لإنتاج مصطلحات JARGON؛ نجد أن هذا يجرض التحسينات على أساس الأساس من حيث قابلية القراءة.
We consider the problem of learning to simplify medical texts. This is important because most reliable, up-to-date information in biomedicine is dense with jargon and thus practically inaccessible to the lay audience. Furthermore, manual simplification does not scale to the rapidly growing body of biomedical literature, motivating the need for automated approaches. Unfortunately, there are no large-scale resources available for this task. In this work we introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinical topics. We then propose a new metric based on likelihood scores from a masked language model pretrained on scientific texts. We show that this automated measure better differentiates between technical and lay summaries than existing heuristics. We introduce and evaluate baseline encoder-decoder Transformer models for simplification and propose a novel augmentation to these in which we explicitly penalize the decoder for producing jargon'' terms; we find that this yields improvements over baselines in terms of readability.
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