Description: 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 communities,
while non-important terms fall into weakly interconnected
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.
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Extracting key terms from noisy and multitheme documents.pdf
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