AUTHOR=Sarker Abeed , Yang Yuan-Chi , Al-Garadi Mohammed Ali , Abbas Aamir 

TITLE=A Light-Weight Text Summarization System for Fast Access to Medical Evidence

JOURNAL=Frontiers in Digital Health

VOLUME=Volume 2 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2020.585559

DOI=10.3389/fdgth.2020.585559

ISSN=2673-253X

ABSTRACT=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. At runtime, our system utilizes similarity measurements derived from pre-trained domain-specific word embeddings in addition to simple features, rather than computationally-expensive knowledge bases and resource-heavy preprocessing methods. Automatic evaluation using ROUGE—the summary evaluation tool—on a public dataset for evidence-based medicine shows that our system's performance, despite the simple implementation, is statistically comparable with the state-of-the-art. Extrinsic manual evaluation based on recently-released COVID19 articles demonstrated that the summarizer performance is close to human agreement, which is generally low, for extractive summarization.