2008 MiningMultiFacetedOverviewsofAr

(Ling et al., 2008) ⇒ Xu Ling, Qiaozhu Mei, ChengXiang Zhai, and Bruce Schatz. (2008). “Mining Multi-faceted Overviews of Arbitrary Topics in a Text Collection.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401952

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Abstract

A common task in many text mining applications is to generate a multi-faceted overview of a topic in a text collection. Such an overview not only directly serves as an informative summary of the topic, but also provides a detailed view of navigation to different facets of the topic. Existing work has cast this problem as a categorization problem and requires training examples for each facet. This has three limitations: (1) All facets are predefined, which may not fit the need of a particular user. (2) Training examples for each facet are often unavailable. (3) Such an approach only works for a predefined type of topics. In this paper, we break these limitations and study a more realistic new setup of the problem, in which we would allow a user to flexibly describe each facet with keywords for an arbitrary topic and attempt to mine a multi-faceted overview in an unsupervised way. We attempt a probabilistic approach to solve this problem. Empirical experiments on different genres of text data show that our approach can effectively generate a multi-faceted overview for arbitrary topics; the generated overviews are comparable with those generated by supervised methods with training examples. They are also more informative than unstructured flat summaries. The method is quite general, thus can be applied to multiple text mining tasks in different application domains.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 MiningMultiFacetedOverviewsofAr	Qiaozhu Mei ChengXiang Zhai Xu Ling Bruce Schatz			Mining Multi-faceted Overviews of Arbitrary Topics in a Text Collection		KDD-2008 Proceedings		10.1145/1401890.1401952		2008