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 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)
In this article, we present a method aiming at building a resource for an information extraction task, from an already existing French predicative lexical resource. We point out the advantages and drawbacks of two predicative resources we worked with: the LADL tables and Volem. We present the reasons why we finally selected Volem as the most interesting(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)