Publication

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

Downloadable Content

Persistent URL
Last modified
  • 05/23/2025
Type of Material
Authors
    Abeed Sarker, Emory UniversityYuan-Chi Yang, Emory UniversityMohammed A Al-Garadi, Emory UniversityAamir Abbas, Carnegie Mellon University
Language
  • English
Date
  • 2020-12-04
Publisher
  • Frontiers
Publication Version
Copyright Statement
  • © 2020 Sarker, Yang, Al-Garadi and Abbas.
License
Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 2
Start Page
  • 585559
End Page
  • 585559
Supplemental Material (URL)
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. Typical medical text summarization approaches require domain knowledge, and the performances of such systems rely on resource-heavy medical domain-specific knowledge sources and pre-processing methods (e.g., text classification) for deriving semantic information. Consequently, these systems are often difficult to speedily customize, extend, or deploy in low-resource settings, and they are often 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 obtain fast access to the latest research evidence. At runtime, our system utilizes similarity measurements derived from pre-trained medical domain-specific word embeddings in addition to simple features, rather than computationally-expensive pre-processing and resource-heavy knowledge bases. Automatic evaluation using ROUGE—a 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 demonstrates that the summarizer performance is close to human agreement, which is generally low, for extractive summarization.
Author Notes
Keywords
Research Categories
  • Engineering, Biomedical

Tools

Relations

In Collection:

Items

Thumbnail Title File Description Date Uploaded Visibility Actions
file details Publication File - w1mxj.pdf Primary Content 2025-05-22 Public Download