EchoGen:从超声心动图笔记生成结论的新基准研究。

Liyan Tang, Shravan Kooragayalu, Yanshan Wang, Ying Ding, Greg Durrett, Justin F Rousseau, Yifan Peng
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引用次数: 3

摘要

最近已经探索了从研究结果中生成摘要(Zhang et al., 2018,2020),例如通常长度较短的放射学报告。在这项工作中,我们将重点放在超声心动图音符上,这些音符比以前的音符类型更长、更复杂。我们正式将超声心动图结论生成(EchoGen)的任务定义为根据发现部分生成结论,重点是关键的心脏发现。为了促进EchoGen方法的发展,我们提出了一个新的基准,它由来自两家医院的两个数据集组成。我们在这个新的基准上进一步比较了标准和最先进的方法,重点是事实的一致性。为了实现这一点,我们开发了一个工具来自动从文本中提取概念属性元组。然后,我们提出了一个评估指标FactComp,用于比较人类参考和生成结论之间的概念属性元组。自动评估和人工评估都表明,在真实性和总体质量方面,人工编写的结论与机器生成的结论之间仍然存在很大差距。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

EchoGen: A New Benchmark Study on Generating Conclusions from Echocardiogram Notes.

EchoGen: A New Benchmark Study on Generating Conclusions from Echocardiogram Notes.

Generating a summary from findings has been recently explored (Zhang et al., 2018, 2020) in note types such as radiology reports that typically have short length. In this work, we focus on echocardiogram notes that is longer and more complex compared to previous note types. We formally define the task of echocardiography conclusion generation (EchoGen) as generating a conclusion given the findings section, with emphasis on key cardiac findings. To promote the development of EchoGen methods, we present a new benchmark, which consists of two datasets collected from two hospitals. We further compare both standard and state-of-the-art methods on this new benchmark, with an emphasis on factual consistency. To accomplish this, we develop a tool to automatically extract concept-attribute tuples from the text. We then propose an evaluation metric, FactComp, to compare concept-attribute tuples between the human reference and generated conclusions. Both automatic and human evaluations show that there is still a significant gap between human-written and machine-generated conclusions on echo reports in terms of factuality and overall quality.

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