@inproceedings{95ba2000b71c48c6a8e0aa0fb8375c34,
title = "An approach for query-focused text summarisation for evidence based medicine",
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%.",
author = "Abeed Sarker and Diego Moll{\'a} and C{\'e}cile Paris",
year = "2013",
doi = "10.1007/978-3-642-38326-7_41",
language = "English",
isbn = "9783642383250",
series = "Lecture Notes in Computer Science",
publisher = "Springer, Springer Nature",
pages = "295--304",
editor = "Niels Peek and {Mar{\'i}n Morales}, Roque and Mor Peleg",
booktitle = "Artificial Intelligence in Medicine",
address = "United States",
note = "14th Conference on Artificial Intelligence in Medicine, AIME 2013 ; Conference date: 29-05-2013 Through 01-06-2013",
}