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Computer Science > Computation and Language

arXiv:2303.05392 (cs)
[Submitted on 7 Mar 2023]

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

Authors:Sanjana Ramprasad, Denis Jered McInerney, Iain J. Marshal, Byron C. Wallace
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Abstract: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.
Subjects: Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
Cite as: arXiv:2303.05392 [cs.CL]
  (or arXiv:2303.05392v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2303.05392

Submission history

From: Sanjana Ramprasad [view email]
[v1] Tue, 7 Mar 2023 17:30:48 UTC (6,691 KB)
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