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• W1982933409 abstract "Highlights? A novel content selection framework for update summarization is proposed. ? Evolutionary manifold-ranking makes the summary novel and relevant to the topic. ? Integrated with spectral clustering is to make the summary have a high coverage. ? The redundancy removal strategy with exponent decay is used in sentence selection. ? The results on TAC 2008-update summarization task show our approach is competitive. Update summarization is a new challenge in automatic text summarization. Different from the traditional static summarization, it deals with the dynamically evolving document collections of a single topic changing over time, which aims to incrementally deliver salient and novel information to a user who has already read the previous documents. How to have a content selection and linguistic quality control in a temporal context are the two new challenges brought by update summarization. In this paper, we address a novel content selection framework based on evolutionary manifold-ranking and normalized spectral clustering. The proposed evolutionary manifold-ranking aims to capture the temporal characteristics and relay propagation of information in dynamic data stream and user need. This approach tries to keep the summary content to be important, novel and relevant to the topic. Incorporation with normalized spectral clustering is to make summary content have a high coverage for each sub-topic. Ordering sub-topics and selecting sentences are dependent on the rank score from evolutionary manifold-ranking and the proposed redundancy removal strategy with exponent decay. The evaluation results on the update summarization task of Text Analysis Conference (TAC) 2008 demonstrate that our proposed approach is competitive. In the 71 run systems, we receive three top 1 under PYRAMID metrics, ranking 13th in ROUGE-2, 15th in ROUGE-SU4 and 21st in BE." @default.
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• W1982933409 date "2012-02-01" @default.
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• W1982933409 title "A novel approach to update summarization using evolutionary manifold-ranking and spectral clustering" @default.
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• W1982933409 doi "https://doi.org/10.1016/j.eswa.2011.08.084" @default.
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