A Light-weight Text Summarization System for Fast Access to Medical Evidence

Overview

As the volume of published medical research continues to grow rapidly, staying up to date with the best-available research evidence regarding specific topics is becoming an increasingly challenging problem for medical experts and researchers. The current COVID19 pandemic is a good example of a topic on which research evidence is rapidly evolving. Automatic query-focused text summarization approaches may help researchers to swiftly review research evidence by presenting salient and query-relevant information from newly-published articles in a condensed manner. However, typical medical text summarization approaches are resource-heavy, and the performances of such systems rely on resource-heavy medical domain-specific knowledge sources and preprocessing methods (e.g., classification or deep learning) for deriving semantic information. Consequently, these systems are often difficult to speedily customize, extend or deploy in low-resource settings, and they are operationally slow. In this paper, we propose a fast and simple extractive summarization approach that can be easily deployed and run, and may thus aid medical experts and researchers to stay up to date regarding the latest research evidence.

Resources

Extrinsic evaluation gold standard (small):

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