We present TrialsSummarizer, a system that aims to automatically summarize evidence presented in the set of randomized controlled trials most relevant to a given query. Building on prior work, the system retrieves trial publications matching a query specifying a combination of condition, intervention(s), and outcome(s), and ranks these according to sample size and estimated study quality. The top-k such studies are passed through a neural multi-document summarization system, yielding a synopsis of these trials. We consider two architectures: A standard sequence-to-sequence model based on BART and a multi-headed architecture intended to provide greater transparency to end-users. Both models produce fluent and relevant summaries of evidence retrieved for queries, but their tendency to introduce unsupported statements render them inappropriate for use in this domain at present. The proposed architecture may help users verify outputs allowing users to trace generated tokens back to inputs.

翻译：我们提出了TrialsSummarizer系统，该系统旨在自动摘要与特定查询最相关的随机对照试验集中所呈现的证据。基于先前研究，该系统检索与指定条件、干预措施和结局组合相匹配的试验出版物，并根据样本量和预估研究质量对这些出版物进行排序。排名前k项的研究被送入神经多文档摘要系统，生成这些试验的概要。我们考虑了两种架构：一种基于BART的标准序列到序列模型，以及一种旨在为最终用户提供更高透明度的多头架构。两种模型都能为查询检索到的证据生成流畅且相关的摘要，但它们倾向于引入未经支持的陈述，这使其目前不适用于该领域。所提出的架构可能帮助用户验证输出，允许用户将生成的标记追溯到输入。