Expert Systems with Applications: An International Journal
Syntactic Extraction Approach to Processing Local Document Collections
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Towards a unified approach to simultaneous single-document and multi-document summarizations
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Expert Systems with Applications: An International Journal
Aspect-based extractive summarization of online reviews
Proceedings of the 2011 ACM Symposium on Applied Computing
Improving document summarization by incorporating social contextual information
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Applied Computational Intelligence and Soft Computing
MCMR: Maximum coverage and minimum redundant text summarization model
Expert Systems with Applications: An International Journal
CDDS: Constraint-driven document summarization models
Expert Systems with Applications: An International Journal
Multiple documents summarization based on evolutionary optimization algorithm
Expert Systems with Applications: An International Journal
Formulation of document summarization as a 0-1 nonlinear programming problem
Computers and Industrial Engineering
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In recent years graph-ranking based algorithms have been proposed for single document summarization and generic multi-document summarization. The algorithms make use of the "votings" or "recommendations" between sentences to evaluate the importance of the sentences in the documents. This study aims to differentiate the cross-document and within-document relationships between sentences for generic multi-document summarization and adapt the graph-ranking based algorithm for topic-focused summarization. The contributions of this study are two-fold: (1) For generic multi-document summarization, we apply the graph-based ranking algorithm based on each kind of sentence relationship and explore their relative importance for summarization performance. (2) For topic-focused multi-document summarization, we propose to integrate the relevance of the sentences to the specified topic into the graph-ranking based method. Each individual kind of sentence relationship is also differentiated and investigated in the algorithm. Experimental results on DUC 2002---DUC 2005 data demonstrate the great importance of the cross-document relationships between sentences for both generic and topic-focused multi-document summarizations. Even the approach based only on the cross-document relationships can perform better than or at least as well as the approaches based on both kinds of relationships between sentences.