%A Maria Grineva
%A Maxim Grinev
%A Dmitry Lizorkin
%T Extracting Key Terms From Noisy and Multi-theme Documents
%X We present a novel method for key term extraction from text
documents. In our method, document is modeled as a graph
of semantic relationships between terms of that document.
We exploit the following remarkable feature of the graph:
the terms related to the main topics of the document tend to
bunch up into densely interconnected subgraphs or commu-
nities, while non-important terms fall into weakly intercon-
nected communities, or even become isolated vertices. We
apply graph community detection techniques to partition
the graph into thematically cohesive groups of terms. We
introduce a criterion function to select groups that contain
key terms discarding groups with unimportant terms. To
weight terms and determine semantic relatedness between
them we exploit information extracted from Wikipedia.
  Using such an approach gives us the following two ad-
vantages. First, it allows effectively processing multi-theme
documents. Second, it is good at filtering out noise infor-
mation in the document, such as, for example, navigational
bars or headers in web pages.
  Evaluations of the method show that it outperforms exist-
ing methods producing key terms with higher precision and
recall. Additional experiments on web pages prove that our
method appears to be substantially more effective on noisy
and multi-theme documents than existing methods.

%C Madrid, Spain
%D 2009
%P 661-661
%L www200967