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We present TrialsSummarizer, a system that aims to automatically summarizeevidence presented in the set of randomized controlled trials most relevant toa given query. Building on prior work, the system retrieves trial publicationsmatching a query specifying a combination of condition, intervention(s), andoutcome(s), and ranks these according to sample size and estimated studyquality. The top-k such studies are passed through a neural multi-documentsummarization system, yielding a synopsis of these trials. We consider twoarchitectures: A standard sequence-to-sequence model based on BART and amulti-headed architecture intended to provide greater transparency toend-users. Both models produce fluent and relevant summaries of evidenceretrieved for queries, but their tendency to introduce unsupported statementsrender them inappropriate for use in this domain at present. The proposedarchitecture may help users verify outputs allowing users to trace generatedtokens back to inputs.

Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges