Degrees of acyclicity for hypergraphs and relational database schemes
Journal of the ACM (JACM)
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
A comparison of document, sentence, and term event spaces
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Using random walks for question-focused sentence retrieval
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Document concept lattice for text understanding and summarization
Information Processing and Management: an International Journal
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Multi-document summarization using cluster-based link analysis
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Image Segmentation as Learning on Hypergraphs
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Extractive summarization using supervised and semi-supervised learning
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Manifold-ranking based topic-focused multi-document summarization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Applying two-level reinforcement ranking in query-oriented multidocument summarization
Journal of the American Society for Information Science and Technology
A document-sensitive graph model for multi-document summarization
Knowledge and Information Systems
Manifold ranking with sink points for update summarization
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Applying regression models to query-focused multi-document summarization
Information Processing and Management: an International Journal
User-Specific Video Summarization
CMSP '11 Proceedings of the 2011 International Conference on Multimedia and Signal Processing - Volume 01
Automatic refinement of keyword annotations for web image search
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
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Traditional graph based sentence ranking algorithms such as LexRank and HITS model the documents to be summarized as a text graph where nodes represent sentences and edges represent pairwise relations. Such modeling cannot capture complex group relationship shared among multiple sentences which can be useful for sentence ranking. In this paper, we propose to take advantage of hypergraph to remedy this defect. In a text hypergraph, nodes still represent sentences, yet hyperedges are allowed to connect more than two sentences. With a text hypergraph, we are thus able to integrate both group relationship and pairwise relationship into a unified framework. Then, a hypergraph based semi-supervised sentence ranking algorithm is developed for query-oriented extractive summarization, where the influence of query is propagated to sentences through the structure of the constructed text hypergraph. When evaluated on DUC datasets, performance of our proposed approach shows improvements compared to a number of baseline systems.