Mining multi-faceted overviews of arbitrary topics in a text collection

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 tex...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Xu Ling, Qiaozhu Mei, ChengXiang Zhai, Bruce R. Schatz
• Keywords: Data Mining, KDD 2008, Multi-faceted Overview, Text Mining, Text Mining Tasks