Development of a corpus for evidence based medicine summarisation

Diego Mollá; Maria Elena Santiago-Martinez

Development of a corpus for evidence based medicine summarisation

Diego Mollá, Maria Elena Santiago-Martinez

Department of Computing

Research output: Contribution to journal › Conference paper

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Abstract

In this paper we introduce some of the key NLP-related problems related to the practice of Evidence Based Medicine and propose the task of multi-document query-focused summarisation as a key approach to solve these problems. We have completed a corpus for the development of such multi-document queryfocused summarisation task. The process to build the corpus combined the use of automated extraction of text, manual annotation, and crowdsourcing to find the reference IDs. We perform a statistical analysis of the corpus for the particular use of single-document summarisation and show that there is still a lot of room for improvement from the current baselines.

Original language	English
Pages (from-to)	86-94
Number of pages	9
Journal	Proceedings of the Australasian Language Technology Association Workshop 2011
Publication status	Published - 2011
Event	Australasian Language Technology Association Workshop (9th : 2011) - Canberra Duration: 1 Dec 2011 → 2 Dec 2011

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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Mq-18117-Publisher version (open access)Final published version, 483 KBLicence: Unspecified

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title = "Development of a corpus for evidence based medicine summarisation",

abstract = "In this paper we introduce some of the key NLP-related problems related to the practice of Evidence Based Medicine and propose the task of multi-document query-focused summarisation as a key approach to solve these problems. We have completed a corpus for the development of such multi-document queryfocused summarisation task. The process to build the corpus combined the use of automated extraction of text, manual annotation, and crowdsourcing to find the reference IDs. We perform a statistical analysis of the corpus for the particular use of single-document summarisation and show that there is still a lot of room for improvement from the current baselines.",

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journal = "Proceedings of the Australasian Language Technology Association Workshop 2011",

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PY - 2011

Y1 - 2011

N2 - In this paper we introduce some of the key NLP-related problems related to the practice of Evidence Based Medicine and propose the task of multi-document query-focused summarisation as a key approach to solve these problems. We have completed a corpus for the development of such multi-document queryfocused summarisation task. The process to build the corpus combined the use of automated extraction of text, manual annotation, and crowdsourcing to find the reference IDs. We perform a statistical analysis of the corpus for the particular use of single-document summarisation and show that there is still a lot of room for improvement from the current baselines.

AB - In this paper we introduce some of the key NLP-related problems related to the practice of Evidence Based Medicine and propose the task of multi-document query-focused summarisation as a key approach to solve these problems. We have completed a corpus for the development of such multi-document queryfocused summarisation task. The process to build the corpus combined the use of automated extraction of text, manual annotation, and crowdsourcing to find the reference IDs. We perform a statistical analysis of the corpus for the particular use of single-document summarisation and show that there is still a lot of room for improvement from the current baselines.

M3 - Conference paper

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EP - 94

JO - Proceedings of the Australasian Language Technology Association Workshop 2011

JF - Proceedings of the Australasian Language Technology Association Workshop 2011

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