Anzeige
Erschienen in:
2009 | OriginalPaper | Buchkapitel
A SVM-Based Ensemble Approach to Multi-Document Summarization
verfasst von : Yllias Chali, Sadid A. Hasan, Shafiq R. Joty
Erschienen in: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
In this paper, we present a Support Vector Machine (SVM) based ensemble approach to combat the extractive multi-document summarization problem. Although SVM can have a good generalization ability, it may experience a performance degradation through wrong classifications. We use a committee of several SVMs, i.e. Cross-Validation Committees (CVC), to form an ensemble of classifiers where the strategy is to improve the performance by correcting errors of one classifier using the accurate output of others. The practicality and effectiveness of this technique is demonstrated using the experimental results.