Preferences in Wikipedia abstracts: Empirical findings and implications for automatic entity summarization
|Preferences in Wikipedia abstracts: Empirical findings and implications for automatic entity summarization|
|Author(s)||Xu D., Cheng G., Qu Y.|
|Published in||Information Processing and Management|
|Keyword(s)||DBpedia, Entity summarization, Feature selection, Property ranking, Wikipedia (Extra: Dbpedia, Empirical findings, Entity summarization, Information overloads, Property ranking, Structured data, Wikipedia, Wikipedia articles, Abstracting, Feature extraction)|
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Preferences in Wikipedia abstracts: Empirical findings and implications for automatic entity summarization is a 2014 journal article written in English by Xu D., Cheng G., Qu Y. and published in Information Processing and Management.
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. © 2013 Elsevier Ltd. All rights reserved.
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