Content Coverage Maximization on Word Networks for Hierarchical Topic Summarization

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Provided by: Association for Computing Machinery
Topic: Big Data
Format: PDF
In this paper, the authors explain about the text summarization by extracting hierarchical topics from a given collection of documents. They propose a new approach of text modeling via network analysis. They convert documents into a word influence network, and find the words summarizing the major topics with an efficient influence maximization algorithm. Besides, the influence capability of the topic words on other words in the network reveal the relations among the topic words. Then they cluster the words and build hierarchies for the topics. Experiments on large collections of web documents show that a simple method based on the influence analysis is effective, compared with existing generative topic modeling and random walk based ranking.
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