Topic discovery based on text mining techniques

  • Pons-Porrata A
  • Berlanga-Llavori R
  • Ruiz-Schulcloper J
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Abstract

In this paper, we present a topic discovery system aimed to reveal
the implicit knowledge present in news streams. This knowledge is
expressed as a hierarchy of topic/subtopics, where each topic contains
the set of documents that are related to it and a summary extracted
from these documents. Summaries so built are useful to browse and
select topics of interest from the generated hierarchies. Our proposal
consists of a new incremental hierarchical clustering algorithm,
which combines both partitional and agglomerative approaches, taking
the main benefits from them. Finally, a new summarization method
based on Testor Theory has been proposed to build the topic summaries.
Experimental results in the TDT2 collection demonstrate its usefulness
and effectiveness not only as a topic detection system, but also
as a classification and summarization tool.

Author-supplied keywords

  • Hierarchical clustering; Text summarization; Topic

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Authors

  • A Pons-Porrata

  • R Berlanga-Llavori

  • J Ruiz-Schulcloper

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