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  • Multi-factors based sentence ordering for cross-document fusion from multimodal content

    Author(s)
    Yue, Lin
    Shi, Zhenkun
    Han, Jiayu
    Wang, Sen
    Chen, Weitong
    Zuo, Wanli
    Griffith University Author(s)
    Wang, Sen
    Year published
    2017
    Metadata
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    Abstract
    Organizing a coherent structure of the sentences extracted from multiple documents, guarantees the fluency and readability of the fused document. In this paper, sentence ordering problem is treated as a combinatorial optimization problem and solved with continuous Hopfield neural network (CHNN). We unify the existing factors by considering the most frequent orders temporal information, and topical relevance between local themes during overall ordering process. Specifically, ordering algorithm traverses all the local themes and locates a shortest path as the final sentence ordering. We show the results with data from Document ...
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    Organizing a coherent structure of the sentences extracted from multiple documents, guarantees the fluency and readability of the fused document. In this paper, sentence ordering problem is treated as a combinatorial optimization problem and solved with continuous Hopfield neural network (CHNN). We unify the existing factors by considering the most frequent orders temporal information, and topical relevance between local themes during overall ordering process. Specifically, ordering algorithm traverses all the local themes and locates a shortest path as the final sentence ordering. We show the results with data from Document Understanding Conferences (DUC) 2002–2005, and demonstrate the effectiveness of the developed approach compared with Random Ordering (RO), Chronological Ordering (CO), Majority Ordering (MO), and Precedence Relation Ordering (PRO).
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    Journal Title
    Neurocomputing
    Volume
    253
    DOI
    https://doi.org/10.1016/j.neucom.2016.12.084
    Subject
    Evolutionary computation
    Procedural content generation
    Engineering
    Psychology
    Publication URI
    http://hdl.handle.net/10072/346254
    Collection
    • Journal articles

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