论文标题
生成RCT的（事实？）叙事摘要：神经多文档摘要的实验
Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization
论文作者
论文摘要
我们考虑从多个试验报告中自动生成叙事生物医学证据摘要的问题。我们使用Cochrane协作成员先前进行的系统评论中相关文章摘要的抽象性摘要评估了现代神经模型，该摘要使用了审查摘要的“作者结论”部分作为我们的目标。我们邀请医学专业人员评估生成的摘要，我们发现现代摘要系统产生了始终如一的流利和相关的概要，但并不总是事实。我们提出了新的方法，这些方法利用了特定于领域的模型来为总结提供信息，例如，明确划定传达关键发现的输入的摘要，并强调了大型和高质量试验的报告。我们发现这些策略适度提高了生成的摘要的事实准确性。最后，我们提出了一种新方法，用于使用推断报告结果的方向性的模型自动评估生成的叙事证据合成的事实。
We consider the problem of automatically generating a narrative biomedical evidence summary from multiple trial reports. We evaluate modern neural models for abstractive summarization of relevant article abstracts from systematic reviews previously conducted by members of the Cochrane collaboration, using the authors conclusions section of the review abstract as our target. We enlist medical professionals to evaluate generated summaries, and we find that modern summarization systems yield consistently fluent and relevant synopses, but that they are not always factual. We propose new approaches that capitalize on domain-specific models to inform summarization, e.g., by explicitly demarcating snippets of inputs that convey key findings, and emphasizing the reports of large and high-quality trials. We find that these strategies modestly improve the factual accuracy of generated summaries. Finally, we propose a new method for automatically evaluating the factuality of generated narrative evidence syntheses using models that infer the directionality of reported findings.
