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A Semi-Automated Approach to Building Text Summarisation Classifiers

Lookup NU author(s): Dr Matias Garcia-Constantino


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An investigation into the extraction of useful information from the free text element of questionnaires, using a semi-automated summarisation extraction technique to generate text summarisation classifiers, is described. A realisation of the proposed technique, SARSET (Semi-Automated Rule Summarisation Extraction Tool), is presented and evaluated using real questionnaire data. The results of this approach are compared against the results obtained using two alternative techniques to build text summarisation classifiers. The first of these uses standard rule-based classifier generators, and the second is founded on the concept of building classifiers using secondary data. The results demonstrate that the proposed semi-automated approach outperforms the other two approaches considered.

Publication metadata

Author(s): Garcia-Constantino MF, Coenen F, Noble PJ, Radford A, Setzkorn C

Editor(s): Petra Perner

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, MLDM 2012

Year of Conference: 2012

Pages: 495-509

ISSN: 0302-9743

Publisher: Springer Berlin Heidelberg


DOI: 10.1007/978-3-642-31537-4_39

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783642315374