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Graph-based ranking algorithms

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  • This paper presents an innovative unsupervised method for automatic sentence extraction using graphbased ranking algorithms. We evaluate the method in the context of a text summarization task, and show that the results obtained compare favorably with previously published results on established benchmarks.

    pdf4p bunbo_1 17-04-2013 26 2   Download

  • We demonstrate TextRank – a system for unsupervised extractive summarization that relies on the application of iterative graphbased ranking algorithms to graphs encoding the cohesive structure of a text. An important characteristic of the system is that it does not rely on any language-specific knowledge resources or any manually constructed training data, and thus it is highly portable to new languages or domains.

    pdf4p bunbo_1 17-04-2013 43 1   Download

  • In bootstrapping (seed set expansion), selecting good seeds and creating stop lists are two effective ways to reduce semantic drift, but these methods generally need human supervision. In this paper, we propose a graphbased approach to helping editors choose effective seeds and stop list instances, applicable to Pantel and Pennacchiotti’s Espresso bootstrapping algorithm. The idea is to select seeds and create a stop list using the rankings of instances and patterns computed by Kleinberg’s HITS algorithm. ...

    pdf7p hongdo_1 12-04-2013 42 2   Download

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