Automated text summarisation and evidence-based medicine: A survey of two domains

TL;DR

This survey reviews the intersection of evidence-based medicine and automated text summarisation, highlighting current techniques, domain-specific needs, and future research directions to manage the growing volume of medical literature.

Contribution

It provides a comprehensive overview of how automated summarisation has been applied to EBM, identifying gaps and guiding future development of domain-specific summarisation methods.

Findings

Existing summarisation techniques are being adapted for medical texts.

Medical practitioners face information overload due to rapid research publication.

The survey highlights the need for cross-domain knowledge in developing effective ATS for EBM.

Abstract

The practice of evidence-based medicine (EBM) urges medical practitioners to utilise the latest research evidence when making clinical decisions. Because of the massive and growing volume of published research on various medical topics, practitioners often find themselves overloaded with information. As such, natural language processing research has recently commenced exploring techniques for performing medical domain-specific automated text summarisation (ATS) techniques-- targeted towards the task of condensing large medical texts. However, the development of effective summarisation techniques for this task requires cross-domain knowledge. We present a survey of EBM, the domain-specific needs for EBM, automated summarisation techniques, and how they have been applied hitherto. We envision that this survey will serve as a first resource for the development of future operational text summarisation techniques for EBM.