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Preferences in Wikipedia abstracts: Empirical findings and implications for automatic entity summarization

机译：Wikipedia摘要中的首选项：自动实体摘要的经验发现和启示

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摘要

The volume of entity-centric structured data grows rapidly on the Web. The description of an entity, composed of property-value pairs (a.k.a. features), has become very large in many applications. To avoid information overload, efforts have been made to automatically select a limited number of features to be shown to the user based on certain criteria, which is called automatic entity summarization. However, to the best of our knowledge, there is a lack of extensive studies on how humans rank and select features in practice, which can provide empirical support and inspire future research. In this article, we present a large-scale statistical analysis of the descriptions of entities provided by DBpedia and the abstracts of their corresponding Wikipedia articles, to empirically study, along several different dimensions, which kinds of features are preferable when humans summarize. Implications for automatic entity summarization are drawn from the findings.
机译：以实体为中心的结构化数据在Web上的增长迅速。由属性-值对（也称为特征）组成的实体描述在许多应用中变得非常庞大。为了避免信息过载，已经做出努力来基于某些标准自动选择要显示给用户的有限数量的功能，这称为自动实体摘要。然而，据我们所知，目前尚缺乏有关人类如何在实践中对等级进行排序和选择特征的广泛研究，这些研究可以提供实证支持并激发未来的研究。在本文中，我们对DBpedia提供的实体描述及其相应的Wikipedia文章的摘要进行了大规模的统计分析，以便从几个不同维度进行实证研究，当人类进行总结时，哪种功能更可取。从结果中得出自动实体摘要的含义。

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