Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Display Omitted  present an approach for query-focused extractive summarisation of medical text.Our approach is data-centric and utilises a specialised annotated corpus. introduce target-sentence-specific summarisation to improve summarisation. incorporate query information and domain-specific knowledge in various ways.Our approach performs significantly better than existing benchmark approaches. BackgroundEvidence-based medicine practice requires medical practitioners to rely on the best available evidence, in addition to their expertise, when making clinical decisions. The medical domain boasts a large amount of published medical research data, indexed in various medical databases such as MEDLINE. As the size of this data grows, practitioners increasingly face the problem of information overload, and past research has established the time-associated obstacles faced by evidence-based medicine practitioners. ,  focus on the problem of automatic text summarisation to help practitioners quickly find query-focused information from relevant documents. Methods utilise an annotated corpus that is specialised for the task of evidence-based summarisation of text. In contrast to past summarisation approaches, which mostly rely on surface level features to identify salient pieces of texts that form the summaries, our approach focuses on the use of corpus-based statistics, and domain-specific lexical knowledge for the identification of summary contents.  also apply a target-sentence-specific summarisation technique that reduces the problem of underfitting that persists in generic summarisation models. ResultsIn automatic evaluations run over a large number of annotated summaries, our extractive summarisation technique statistically outperforms various baseline and benchmark summarisation models with a percentile rank of 96.8%. A manual evaluation shows that our extractive summarisation approach is capable of selecting content with high recall and precision, and may thus be used to generate bottom-line ansrs to practitioners' queries. ConclusionsOur research shows that the incorporation of specialised data and domain-specific knowledge can significantly improve text summarisation performance in the medical domain. Due to the vast amounts of medical text available, and the high growth of this form of data,  suspect that such summarisation techniques will address the time-related obstacles associated with evidence-based medicine.
['Abeed Sarker', 'Diego Mollá', 'Cécile Paris']
I am writing a research paper on a idea Query-oriented evidence extraction to support evidence-based medicine practice
[{"from": "human", "value": "I am writing a research paper on a idea Query-oriented evidence extraction to support evidence-based medicine practice"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Display Omitted  present an approach for query-focused extractive summarisation of medical text.Our approach is data-centric and utilises a specialised annotated corpus. introduce target-sentence-specific summarisation to improve summarisation. incorporate query information and domain-specific knowledge in various ways.Our approach performs significantly better than existing benchmark approaches. BackgroundEvidence-based medicine practice requires medical practitioners to rely on the best available evidence, in addition to their expertise, when making clinical decisions. The medical domain boasts a large amount of published medical research data, indexed in various medical databases such as MEDLINE. As the size of this data grows, practitioners increasingly face the problem of information overload, and past research has established the time-associated obstacles faced by evidence-based medicine practitioners. ,  focus on the problem of automatic text summarisation to help practitioners quickly find query-focused information from relevant documents. Methods utilise an annotated corpus that is specialised for the task of evidence-based summarisation of text. In contrast to past summarisation approaches, which mostly rely on surface level features to identify salient pieces of texts that form the summaries, our approach focuses on the use of corpus-based statistics, and domain-specific lexical knowledge for the identification of summary contents.  also apply a target-sentence-specific summarisation technique that reduces the problem of underfitting that persists in generic summarisation models. ResultsIn automatic evaluations run over a large number of annotated summaries, our extractive summarisation technique statistically outperforms various baseline and benchmark summarisation models with a percentile rank of 96.8%. A manual evaluation shows that our extractive summarisation approach is capable of selecting content with high recall and precision, and may thus be used to generate bottom-line ansrs to practitioners' queries. ConclusionsOur research shows that the incorporation of specialised data and domain-specific knowledge can significantly improve text summarisation performance in the medical domain. Due to the vast amounts of medical text available, and the high growth of this form of data,  suspect that such summarisation techniques will address the time-related obstacles associated with evidence-based medicine."}]