  Clinical trials offer a fundamental opportunity to discover new treatments
and advance the medical knowledge. However, the uncertainty of the outcome of a
trial can lead to unforeseen costs and setbacks. In this study, we propose a
new method to predict the effectiveness of an intervention in a clinical trial.
Our method relies on generating an informative summary from multiple documents
available in the literature about the intervention under study. Specifically,
our method first gathers all the abstracts of PubMed articles related to the
intervention. Then, an evidence sentence, which conveys information about the
effectiveness of the intervention, is extracted automatically from each
abstract. Based on the set of evidence sentences extracted from the abstracts,
a short summary about the intervention is constructed. Finally, the produced
summaries are used to train a BERT-based classifier, in order to infer the
effectiveness of an intervention. To evaluate our proposed method, we introduce
a new dataset which is a collection of clinical trials together with their
associated PubMed articles. Our experiments, demonstrate the effectiveness of
producing short informative summaries and using them to predict the
effectiveness of an intervention.
