AbstractThe performances of current medical text summarization systems rely on resource-heavy domain-specific knowledge sources, and preprocessing methods (e.g., classification or deep learning) for deriving semantic information. Consequently, these systems are often difficult to customize, extend or deploy in low-resource settings, and are operationally slow. We propose a fast summarization system that can aid practitioners at point-of-care, and, thus, improve evidence-based healthcare. At runtime, our system utilizes similarity measurements derived from pre-trained domain-specific word embeddings in addition to simple features, rather than clunky knowledge bases and resource-heavy preprocessing. Automatic evaluation 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.
Robot Learning of Domain Specific Knowledge from Natural Language Sources