Abstract
We present an approach for extractive, query-focused, single-document summarisation of medical text. Our approach utilises a combination of target-sentence-specific and target-sentence-independent statistics derived from a corpus specialised for summarisation in the medical domain. We incorporate domain knowledge via the application of multiple domain-specific features, and we customise the answer extraction process for different question types. The use of carefully selected domain-specific features enables our summariser to generate content-rich extractive summaries, and an automatic evaluation of our system reveals that it outperforms other baseline and benchmark summarisation systems with a percentile rank of 96.8%.
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References
Athenikos, S.J., Han, H.: Biomedical question answering: A survey. Computer Methods and Programs in Biomedicine, 1–24 (2009)
Cao, Y., Liu, F., Simpson, P., Antieau, L.D., Bennett, A., Cimino, J.J., Ely, J.W., Yu, H.: AskHermes: An Online Question Answering System for Complex Clinical Questions. Journal of Biomedical Informatics 44(2), 277–288 (2011)
Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of SIGIR, pp. 335–336 (1998)
Ceylan, H., Mihalcea, R., Özertem, U., Lloret, E., Palomar, M.: Quantifying the limits and success of extractive summarization systems across domains. In: Proceedings of NAACL, pp. 903–911 (2010)
Chang, C.-C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 1–27 (2011)
Edmundson, H.P.: New methods in automatic extracting. J. ACM 16(2), 264–285 (1969)
Ely, J.W., Osheroff, J.A., Ebell, M.H., Bergus, G.R., Levy, B.T., Chambliss, L.M., Evans, E.R.: Analysis of questions asked by family doctors regarding patient care. BMJ 319(7206), 358–361 (1999)
Kim, S.N.N., Martinez, D., Cavedon, L., Yencken, L.: Automatic classification of sentences to support Evidence Based Medicine. BMC Bioinformatics 12(2) (2011)
Lin, C.Y., Hovy, E.: Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics. In: Proceedings of HLT-NAACL 2003, pp. 71–78 (2003)
Lin, J.J., Demner-Fushman, D.: Answering clinical questions with knowledge-based and statistical techniques. Computational Linguistics 33(1), 63–103 (2007)
Mollá-Aliod, D., Santiago-Martinez, M.E.: Development of a Corpus for Evidence Based Medicine Summarisation. In: Proceedings of ALTW, pp. 86–94 (2011)
Nenkova, A., Passonneau, R.: The impact of frequency on summarization. MSR-TR, Microsoft Research, Redmond, Washington (2005)
Niu, Y., Zhu, X., Hirst, G.: Using outcome polarity in sentence extraction for medical question-answering. In: Proceedings of the AMIA Annual Symposium, pp. 599–603 (2006)
Richardson, S.W., Wilson, M.C., Nishikawa, J., Hayward, R.S.: The well-built clinical question: A key to evidence-based decisions. ACP Journal Club 123(3), A12–A13 (1995)
Sarker, A., Mollá, D., Paris, C.: Extractive Evidence Based Medicine Summarisation Based on Sentence-Specific Statistics. In: Proceedings of the 25th IEEE International Symposium on CBMS, pp. 1–4 (2012)
Schilder, F., Kondadadi, R.: Fastsum: Fast and accurate query-based multi-document summarization. In: Proceedings of ACL-HLT, Short Papers, pp. 205–208 (2008)
Yu, H., Cao, Y.G.: Automatically extracting information needs from ad hoc clinical questions. In: AMIA Annu. Symp. Proc., pp. 96–100 (2008)
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Sarker, A., Mollá, D., Paris, C. (2013). An Approach for Query-Focused Text Summarisation for Evidence Based Medicine. In: Peek, N., Marín Morales, R., Peleg, M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science(), vol 7885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38326-7_41
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DOI: https://doi.org/10.1007/978-3-642-38326-7_41
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