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首页> 外文期刊>Computational Intelligence >SUBTOPIC-BASED MULTIMODALITY RANKING FOR TOPIC-FOCUSED MULTIDOCUMENT SUMMARIZATION
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SUBTOPIC-BASED MULTIMODALITY RANKING FOR TOPIC-FOCUSED MULTIDOCUMENT SUMMARIZATION

机译：基于主题的多文档摘要的基于主题的多模态排序

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摘要

Topic-focused multidocument summarization has been a challenging task because the created summary is required to be biased to the given topic or query. Existing methods consider the given topic as a single coarse unit, and then directly incorporate the relevance between each sentence and the single topic into the sentence evaluation process. However, the given topic is usually not well defined and it consists of a few explicit or implicit subtopics. In this study, the related subtopics are discovered from the topic's narrative text or document set through topic analysis techniques. Then, the sentence relationships against each subtopic are considered as an individual modality and the multimodality manifold-ranking method is proposed to evaluate and rank sentences by fusing the multiple modalities. Experimental results on the DUC benchmark data sets show the promising results of our proposed methods.
机译：聚焦主题的多文档摘要是一项具有挑战性的任务，因为需要将创建的摘要偏向给定的主题或查询。现有方法将给定主题视为单个粗略单位，然后将每个句子与单个主题之间的相关性直接纳入句子评估过程。但是，给定的主题通常没有很好地定义，并且由一些显式或隐式子主题组成。在本研究中，通过主题分析技术从主题的叙述文本或文档集中发现了相关的子主题。然后，针对每个子主题的句子关系被认为是一个单独的模态，并且提出了多模态流形排序方法，通过融合多种模态来对句子进行评估和排序。在DUC基准数据集上的实验结果显示了我们提出的方法的有希望的结果。

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