论文标题
参加医学本体论：临床抽象性摘要的内容选择
Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization
论文作者
论文摘要
序列到序列（SEQ2SEQ）网络是文本摘要任务的完善模型。它可以学会产生可读的内容；但是，它在有效地识别来源的关键区域方面跌落不足。在本文中，我们通过将显着本体论术语扩展到汇总器中来解决临床抽象摘要的内容选择问题。 Our experiments on two publicly available clinical data sets (107,372 reports of MIMIC-CXR, and 3,366 reports of OpenI) show that our model statistically significantly boosts state-of-the-art results in terms of Rouge metrics (with improvements: 2.9% RG-1, 2.5% RG-2, 1.9% RG-L), in the healthcare domain where any range of improvement impacts patients' welfare.
Sequence-to-sequence (seq2seq) network is a well-established model for text summarization task. It can learn to produce readable content; however, it falls short in effectively identifying key regions of the source. In this paper, we approach the content selection problem for clinical abstractive summarization by augmenting salient ontological terms into the summarizer. Our experiments on two publicly available clinical data sets (107,372 reports of MIMIC-CXR, and 3,366 reports of OpenI) show that our model statistically significantly boosts state-of-the-art results in terms of Rouge metrics (with improvements: 2.9% RG-1, 2.5% RG-2, 1.9% RG-L), in the healthcare domain where any range of improvement impacts patients' welfare.
