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This paper examines a new approach to information distillation over temporally ordered documents, and proposes a novel evaluation scheme for such a framework. It combines the strengths of and extends beyond conventional adaptive filtering, novelty detection and non-redundant passage ranking with respect to long-lasting information needs ('tasks' with multiple queries). Our approach supports fine-grained user feedback via highlighting of arbitrary spans of text, and leverages such informationdoi:10.1145/1277741.1277750 dblp:conf/sigir/YangLLHKR07 fatcat:7zbihodsf5aftnkvpf3zdcgyz4