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Insertion of Ontological Knowledge to Improve Automatic Summarization Extraction Methods

Author(s): Jésus Antonio Motta | Laurence Capus | Nicole Tourigny

Journal: Journal of Intelligent Learning Systems and Applications
ISSN 2150-8402

Volume: 03;
Issue: 03;
Start page: 131;
Date: 2011;
Original page

Keywords: Automatic Summarization | Ontology | Machine Learning | Extraction Method

The vast availability of information sources has created a need for research on automatic summarization. Current methods perform either by extraction or abstraction. The extraction methods are interesting, because they are robust and independent of the language used. An extractive summary is obtained by selecting sentences of the original source based on information content. This selection can be automated using a classification function induced by a machine learning algorithm. This function classifies sentences into two groups: important or non-important. The important sentences then form the summary. But, the efficiency of this function directly depends on the used training set to induce it. This paper proposes an original way of optimizing this training set by inserting lexemes obtained from ontological knowledge bases. The training set optimized is reinforced by ontological knowledge. An experiment with four machine learning algorithms was made to validate this proposition. The improvement achieved is clearly significant for each of these algorithms.

Tango Jona
Tangokurs Rapperswil-Jona

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