Questionnaire Free Text Summarisation Using Hierarchical Classification

Abstract

This paper presents an investigation into the summarisation of the free text element of questionnaire data using hierarchical text classification. The process makes the assumption that text summarisation can be achieved using a classification approach whereby several class labels can be associated with documents which then constitute the summarisation. A hierarchical classification approach is suggested which offers the advantage that different levels of classification can be used and the summarisation customised according to which branch of the tree the current document is located. The approach is evaluated using free text from questionnaires used in the SAVSNET (Small Animal Veterinary Surveillance Network) project. The results demonstrate the viability of using hierarchical classification to generate free text summaries.

Publication
In Proceedings - Research and Development in Intelligent Systems XXIX. Incorporating Applications and Innovations in Intelligent Systems XX (AI2012)
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Matias Garcia-Constantino
Lecturer in Computing Science

My research interests include Data Analysis, Internet of Things (IoT), Artificial Intelligence, Natural Language Processing and Network Science. matter.