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

Xu Ling, Qiaozhu Mei, Chengxiang Zhai, Bruce Schatz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationKDD 2008 - Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining
Pages497-505
Number of pages9
DOIs
StatePublished - 2008
Event14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008 - Las Vegas, NV, United States
Duration: Aug 24 2008Aug 27 2008

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008
Country/TerritoryUnited States
CityLas Vegas, NV
Period8/24/088/27/08

Keywords

  • Algorithams

ASJC Scopus subject areas

  • Software
  • Information Systems

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