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Coherent narrative summarization with a cognitive model

机译:认知模型的连贯叙事总结

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For summary readers, coherence is no less important than informativeness and is ultimately measured in human terms. Taking a human cognitive perspective, this paper is aimed to generate coherent summaries of narrative text by developing a cognitive model. To model coherence with a cognitive background, we simulate the long-term human memory by building a semantic network from a large corpus like Wiki and design algorithms to account for the information flow among different compartments of human memory. Proposition is the basic processing unit for the model. After processing a whole narrative in a cyclic way, our model supplies information to be used for extractive summarization on the proposition level. Experimental results on two kinds of narrative text, newswire articles and fairy tales, show the superiority of our proposed model to several representative and popular methods.
机译:对于总结性读者而言,连贯性不亚于提供信息的重要性,而且连贯性最终是以人为标准来衡量的。从人类认知的角度出发,本文旨在通过建立认知模型来生成叙述文本的连贯性摘要。为了对具有认知背景的连贯性进行建模,我们通过从大型语料库(如Wiki)构建语义网络来模拟人类的长期记忆,并设计算法来说明人类记忆的不同部分之间的信息流。命题是模型的基本处理单元。在以循环方式处理整个叙述之后,我们的模型提供了用于命题级别的提取性摘要的信息。对两种叙述性文本(新闻专线文章和童话故事)的实验结果表明,我们提出的模型优于几种代表性的流行方法。

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