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)
In this paper, we present a summarization system that is specifically designed to process blog posts, where factual information is mixed with opinions on the discussed facts. Our approach combines redundancy analysis with new information tracking and is enriched by a module that computes the polarity of textual fragments in order to summarize blog posts(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)
Automatic Text Summarization (TS) has been a topic of interest in Natural Language Processing for a long time. Many research groups and companies in Canada actively pursue this topic, yet there has never been in Canada a meeting devoted to TS. This workshop, for the first time, brings together Canadian researchers working on TS; we also warmly welcome(More)