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 sentence-specific 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%.
| Language | English |
|---|---|
| Title of host publication | Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012 |
| Editors | Paolo Soda, Francesco Tortorella |
| Place of Publication | Piscataway, NJ |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 1-4 |
| Number of pages | 4 |
| ISBN (Print) | 9781467320511 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012 - Rome, Italy Duration: 20 Jun 2012 → 22 Jun 2012 |
Other
| Other | 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012 |
|---|---|
| Country | Italy |
| City | Rome |
| Period | 20/06/12 → 22/06/12 |
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Extractive evidence based medicine summarisation based on sentence-specific statistics. / Sarker, Abeed; Mollá, Diego; Paris, Cécile.
Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012. ed. / Paolo Soda; Francesco Tortorella. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2012. p. 1-4 6266373.Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › Research › peer-review
TY - GEN
T1 - Extractive evidence based medicine summarisation based on sentence-specific statistics
AU - Sarker, Abeed
AU - Mollá, Diego
AU - Paris, Cécile
PY - 2012
Y1 - 2012
N2 - 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 sentence-specific 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%.
AB - 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 sentence-specific 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%.
UR - http://www.scopus.com/inward/record.url?scp=84867311655&partnerID=8YFLogxK
U2 - 10.1109/CBMS.2012.6266373
DO - 10.1109/CBMS.2012.6266373
M3 - Conference proceeding contribution
SN - 9781467320511
SP - 1
EP - 4
BT - Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012
A2 - Soda, Paolo
A2 - Tortorella, Francesco
PB - Institute of Electrical and Electronics Engineers (IEEE)
CY - Piscataway, NJ
ER -