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1.  Extractive summarisation of medical documents using domain knowledge and corpus statistics 
The Australasian Medical Journal  2012;5(9):478-481.
Evidence Based Medicine (EBM) practice requires practitioners to extract evidence from published medical research when answering clinical queries. Due to the time- consuming nature of this practice, there is a strong motivation for systems that can automatically summarise medical documents and help practitioners find relevant information.
The aim of this work is to propose an automatic query- focused, extractive summarisation approach that selects informative sentences from medical documents.
We use a corpus that is specifically designed for summarisation in the EBM domain. We use approximately half the corpus for deriving important statistics associated with the best possible extractive summaries. We take into account factors such as sentence position, length, sentence content, and the type of the query posed. Using the statistics from the first set, we evaluate our approach on a separate set. Evaluation of the qualities of the generated summaries is performed automatically using ROUGE, which is a popular tool for evaluating automatic summaries.
Our summarisation approach outperforms all baselines (best baseline score: 0.1594; our score 0.1653). Further improvements are achieved when query types are taken into account.
The quality of extractive summarisation in the medical domain can be significantly improved by incorporating domain knowledge and statistics derived from a specialised corpus. Such techniques can therefore be applied for content selection in end-to-end summarisation systems.
PMCID: PMC3477776  PMID: 23115581
Automatic summarisation; extractive summarisation evidence based medicine; medical document summarisation
2.  Creation of a corpus for evidence based medicine summarisation 
The Australasian Medical Journal  2012;5(9):503-506.
Automated text summarisers that find the best clinical evidence reported in collections of medical literature are of potential benefit for the practice of Evidence Based Medicine (EBM). Research and development of text summarisers for EBM, however, is impeded by the lack of corpora to train and test such systems.
To produce a corpus for research in EBM summarisation.
We sourced the “Clinical Inquiries” section of the Journal of Family Practice (JFP) and obtained a sizeable sample of questions and evidence based summaries. We further processed the summaries by combining automated techniques, human annotations, and crowdsourcing techniques to identify the PubMed IDs of the references.
The corpus has 456 questions, 1,396 answer components, 3,036 answer justifications, and 2,908 references.
The corpus is now available for the research community at
PMCID: PMC3477779  PMID: 23115585
Evidence Based Medicine; corpora; text summarisation; natural language processing.

Results 1-2 (2)