Title :
Extractive evidence based medicine summarisation based on sentence-specific statistics
Author :
Sarker, Abeed ; Mollá, Diego ; Paris, Cécile
Author_Institution :
Dept. of Comput., Macquarie Univ., Sydney, NSW, Australia
Abstract :
We present an approach for extracting 3-sentence evidence-based summaries relevant to clinical questions. We approach this task as one of query-focused, extractive, single-document summarisation using sentencespecific statistics for each target sentence. We incorporate simple statistics and domain knowledge and show that such an approach is effective for identifying informative sentences from medical abstracts. Our system is evaluated automatically using ROUGE, and we compare our results with several baselines. The ROUGE-L F-scores of our system outperform all baselines. In addition, our approach is computationally efficient, and, on a percentile rank measure, our system achieves a percentile rank of 97.3%.
Keywords :
document handling; medical information systems; query processing; statistical analysis; 3-sentence evidence-based summaries extraction; ROUGE-L F-scores; clinical questions; extractive evidence based medicine summarisation; informative sentence identification; medical abstracts; sentence-specific statistics; single-document summarisation; Abstracts; Approximation methods; Equations; Probability density function; Semantics; Silicon; Training;
Conference_Titel :
Computer-Based Medical Systems (CBMS), 2012 25th International Symposium on
Conference_Location :
Rome
Print_ISBN :
978-1-4673-2049-8
DOI :
10.1109/CBMS.2012.6266373