  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.
