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Information distillation aims to extract the most useful pieces of information related to a given query from massive, possibly multilingual, audio and textual document sources. One critical component in a distillation engine is detecting sentences to be extracted from each relevant document. In this paper, we present a statistical sentence extraction approach for distillation. Basically, we frame this tack as a classification problem, where each candidate sentence in documents is classified as a relevant to the query or not. These documents may be textual or audio format and in a number of languages. For audio documents, we use both manual and automatic transcriptions, for non-English documents, we use automatic translations. In this work, we use AdaBoost, a discriminative classification method with both lexical and semantic features. The results indicate 11%-13% relative improvement over a baseline keyword-spotting-based approach. We also show the robustness of our method on the audio subset of the document sources using manual and automatic transcriptions.