@inproceedings{fe953cca97d0464ca53edb2f917813a4,
title = "Event phase extraction and summarization",
abstract = "Text summarization aims to generate a single,concise representation for documents. For Web applications,documents related to an event retrieved by search engines usually describe several event phases implicitly,making it difficult for existing approaches to identify,extract and summarize these phases. In this paper,we aim to mine and summarize event phases automatically from a stream of news data on the Web. We model the semantic relations of news via a graph model called Temporal Content Coherence Graph. A structural clustering algorithm EPCluster is designed to separate news articles corresponding to event phases. After that,we calculate the relevance of news articles based on a vertex-reinforced random walk algorithm and generate event phase summaries in a relevance maximum optimization framework. Experiments on news datasets illustrate the effectiveness of our approach.",
keywords = "Event phase summarization, Structural clustering, Vertex-reinforced random walk",
author = "Chengyu Wang and Rong Zhang and Xiaofeng He and Guomin Zhou and Aoying Zhou",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 17th International Conference on Web Information Systems Engineering, WISE 2016 ; Conference date: 08-11-2016 Through 10-11-2016",
year = "2016",
doi = "10.1007/978-3-319-48740-3\_35",
language = "英语",
isbn = "9783319487397",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "473--488",
editor = "Wojciech Cellary and Jianmin Wang and Mokbel, \{Mohamed F.\} and Hua Wang and Rui Zhou and Yanchun Zhang",
booktitle = "Web Information Systems Engineering – WISE 2016 - 17th International Conference, Proceedings",
address = "德国",
}