Extractive evidence based medicine summarisation based on sentence-specific statistics

Abeed Sarker; Diego Mollá; Cécile Paris

doi:10.1109/CBMS.2012.6266373

Extractive evidence based medicine summarisation based on sentence-specific statistics

Abeed Sarker^*, Diego Mollá, Cécile Paris

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

1 Citation (Scopus)

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%.

Original 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	https://doi.org/10.1109/CBMS.2012.6266373
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/Territory	Italy
City	Rome
Period	20/06/12 → 22/06/12

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10.1109/CBMS.2012.6266373

Cite this

Sarker, A., Mollá, D., & Paris, C. (2012). Extractive evidence based medicine summarisation based on sentence-specific statistics. In P. Soda, & F. Tortorella (Eds.), Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012 (pp. 1-4). Article 6266373 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CBMS.2012.6266373

Sarker, Abeed ; Mollá, Diego ; Paris, Cécile. / Extractive evidence based medicine summarisation based on sentence-specific statistics. Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012. editor / Paolo Soda ; Francesco Tortorella. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2012. pp. 1-4

@inproceedings{0f32bf5e06854b32a2cd8e468c33f959,

title = "Extractive evidence based medicine summarisation based on sentence-specific statistics",

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%.",

author = "Abeed Sarker and Diego Moll{\'a} and C{\'e}cile Paris",

year = "2012",

doi = "10.1109/CBMS.2012.6266373",

language = "English",

isbn = "9781467320511",

pages = "1--4",

editor = "Paolo Soda and Francesco Tortorella",

booktitle = "Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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note = "25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012 ; Conference date: 20-06-2012 Through 22-06-2012",

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Sarker, A, Mollá, D & Paris, C 2012, Extractive evidence based medicine summarisation based on sentence-specific statistics. in P Soda & F Tortorella (eds), Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012., 6266373, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 1-4, 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012, Rome, Italy, 20/06/12. https://doi.org/10.1109/CBMS.2012.6266373

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 › peer-review

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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%.

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DO - 10.1109/CBMS.2012.6266373

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BT - Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012

A2 - Soda, Paolo

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Sarker A, Mollá D , Paris C. Extractive evidence based medicine summarisation based on sentence-specific statistics. In Soda P, Tortorella F, editors, Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2012. p. 1-4. 6266373 doi: 10.1109/CBMS.2012.6266373

Extractive evidence based medicine summarisation based on sentence-specific statistics

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