# Automatic Summarization System coupled with a Question-Answering System (QAAS) Source: [Automatic Summarization System coupled with a Question-Answering System (QAAS)]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:24.817Z Authors: Juan-Manuel Torres-Moreno, Pier-Luc St-Onge, Michel Gagnon, Marc El-B\\\`eze, Patrice Bellot Published: 2009-05-18T21:35:52.000Z ## Abstract To select the most relevant sentences of a document, it uses an optimal decision algorithm that combines several metrics. The metrics processes, weighting and extract pertinence sentences by statistical and informational algorithms. This technique might improve a Question-Answering system, whose function is to provide an exact answer to a question in natural language. In this paper, we present the results obtained by coupling the Cortex summarizer with a Question-Answering system (QAAS). Two configurations have been evaluated. In the first one, a low compression level is selected and the summarization system is only used as a noise filter. In the second configuration, the system actually functions as a summarizer, with a very high level of compression. Our results on French corpus demonstrate that the coupling of Automatic Summarization system with a Question-Answering system is promising. Then the system has been adapted to generate a customized summary depending on the specific question. Tests on a french multi-document corpus have been realized, and the personalized QAAS system obtains the best performances. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()