Aurélien Bossard

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In this paper, we present a novel approach for automatic summarization. Our system, called CBSEAS, integrates a new method to detect redundancy at its very core, and produce more expressive summaries than previous approaches. Moreover, we show that our system is versatile enough to integrate opinion mining techniques, so that it is capable of producing(More)
RÉSUMÉ. Dans cet article, nous comparons les résultats produits par différentes approches de résumé multi-documents. Nous opposons deux approches classiques à la nôtre qui place la modélisation de la diversité informationnelle du corpus au centre du processus. Nous évaluons également l'impact de différentes mesures de similarité entre phrases. Les(More)
In this paper, we present a novel approach for automatic summarization. CBSEAS, the system implementing this approach, integrates a method to detect redundancy at its very core, in order to produce more expressive summaries than previous approaches. The evaluation of our system during TAC 2008 —the Text Analysis Conference— revealed that, even if our system(More)