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Register a new userWhat's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization
ما في ملخص؟وضع الأساس للتقدم في تلخيص الدورة المستشفى
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تلخيص الروايات السريرية هي مشكلة بحثية طويلة الأمد.هنا، نقدم مهمة تلخيص الدورة بالمستشفى.بالنظر إلى الوثائق التي تأليفها طوال دخول المستشفى المريض، تولد فقرة تحكي قصة قبول المريض.نحن نبني مجموعة بيانات إنجليزية ونص نصية تبلغ 109000 من المستشفيات (ملاحظات مصدر 2M) وكيل الموجز المقابل الخاص بهم: الفقرة الدورة التدريبية المختصرة الأيمن من الطبيب الكبرى كجزء من مذكرة التفريغ.تكشف التحليلات الاستكشافية أن فقرات BHC مبادرة للغاية مع بعض الشظايا المستخرجة الطويلة؛موجزة شاملةتختلف في مؤسسة النمط والمحتوى من الملاحظات المصدر؛عرض الحد الأدنى التماسك المعجمي؛وتمثيل المراجع الفضية القياسية.يحدد تحليلنا آثار متعددة على تصميم هذه المهمة المعقدة متعددة الوثائق.
Summarization of clinical narratives is a long-standing research problem. Here, we introduce the task of hospital-course summarization. Given the documentation authored throughout a patient's hospitalization, generate a paragraph that tells the story of the patient admission. We construct an English, text-to-text dataset of 109,000 hospitalizations (2M source notes) and their corresponding summary proxy: the clinician-authored Brief Hospital Course'' paragraph written as part of a discharge note. Exploratory analyses reveal that the BHC paragraphs are highly abstractive with some long extracted fragments; are concise yet comprehensive; differ in style and content organization from the source notes; exhibit minimal lexical cohesion; and represent silver-standard references. Our analysis identifies multiple implications for modeling this complex, multi-document summarization task.
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Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series. However, real-world dialogues pose a great challenge to current summarization models, as the dialogue length typically ex
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Modern summarization models generate highly fluent but often factually unreliable outputs. This motivated a surge of metrics attempting to measure the factuality of automatically generated summaries. Due to the lack of common benchmarks, these metric
s cannot be compared. Moreover, all these methods treat factuality as a binary concept and fail to provide deeper insights on the kinds of inconsistencies made by different systems. To address these limitations, we devise a typology of factual errors and use it to collect human annotations of generated summaries from state-of-the-art summarization systems for the CNN/DM and XSum datasets. Through these annotations we identify the proportion of different categories of factual errors and benchmark factuality metrics, showing their correlation with human judgement as well as their specific strengths and weaknesses.
Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization. In this work, we explore four different challenges of the task: handling and differentiating parts of the dialogue belonging to mul
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