@inproceedings{f71397e0e21c4b03973c08796f37c346,
title = "Double-hypergraph based sentence ranking for query-focused multi-document summarizaton",
abstract = "Traditional graph based sentence ranking approaches modeled the documents as a text graph where vertices represent sentences and edges represent pairwise similarity relationships between two sentences. Such modeling cannot capture complex group relationships shared among multiple sentences which can be useful for sentence ranking. In this paper, we propose two different group relationships (sentence-topic relationship and document-topic relationship) shared among sentences, and construct a double-hypergraph integrating these relationships into a unified framework. Then, a double-hypergraph based sentence ranking algorithm is developed for query-focused multi-document summarization, in which Markov random walk is defined on each hypergraph and the mixture Markov chains are formed so as to perform transductive learning in the double-hypergraph. When evaluated on DUC datasets, performance of the proposed approach is remarkable.",
keywords = "Hypergraph, Query-focused multi-document summarization, Sentence ranking",
author = "Xiaoyan Cai and Junwei Han and Lei Guo and Libin Yang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops, WIW 2016 ; Conference date: 13-10-2016 Through 16-10-2016",
year = "2017",
month = jan,
day = "11",
doi = "10.1109/WIW.2016.17",
language = "英语",
series = "Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops, WIW 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "112--118",
booktitle = "Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops, WIW 2016",
}