@inproceedings{97b1724f6fb44e5dabe3204b5e5f8d90,
title = "Content coverage maximization on word networks for hierarchical topic summarization",
abstract = "This paper studies text summarization by extracting hierarchical topics from a given collection of documents. We propose a new approach of text modeling via network analysis. We 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 we 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. Copyright is held by the owner/author(s).",
keywords = "Information coverage, Keyword extraction, Text summarization, Topic hierarchy",
author = "Chi Wang and Xiao Yu and Yanen Li and Chengxiang Zhai and Jiawei Han",
year = "2013",
doi = "10.1145/2505515.2505585",
language = "English (US)",
isbn = "9781450322638",
series = "International Conference on Information and Knowledge Management, Proceedings",
pages = "249--258",
booktitle = "CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management",
note = "22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 ; Conference date: 27-10-2013 Through 01-11-2013",
}