The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Creating and evaluating multi-document sentence extract summaries
Proceedings of the ninth international conference on Information and knowledge management
Recent developments in text summarization
Proceedings of the tenth international conference on Information and knowledge management
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Generating natural language summaries from multiple on-line sources
Computational Linguistics - Special issue on natural language generation
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Information fusion in the context of multi-document summarization
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Topic themes for multi-document summarization
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Multi-document summarization by graph search and matching
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
AdaSum: an adaptive model for summarization
Proceedings of the 17th ACM conference on Information and knowledge management
Generating aspect-oriented multi-document summarization with event-aspect model
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
MCMR: Maximum coverage and minimum redundant text summarization model
Expert Systems with Applications: An International Journal
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Multi-document summarization (MDS) is a challenging research topic in natural language processing. In order to obtain an effective summary, this paper presents a novel extractive approach based on graph-based sub-topic partition algorithm (GSPSummary). In particular, a sub-topic model based on graph representation is presented with emphasis on the implicit logic structure of the topic covered in the document collection. Then, a new framework of MDS with sub-topic partition is proposed. Furthermore, a novel scalable ranking criterion is adopted, in which both word based features and global features are integrated together. Experimental results on DUC2005 show that the proposed approach can significantly outperform existing approaches of the top performing systems in DUC tasks.