Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
The automatic construction of large-scale corpora for summarization research
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Extracting sentence segments for text summarization: a machine learning approach
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Generic text summarization using relevance measure and latent semantic analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A new approach to unsupervised text summarization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
The use of unlabeled data to improve supervised learning for text summarization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Web-page summarization using clickthrough data
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Topic themes for multi-document summarization
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Personalized Summarization Agent Using Non-negative Matrix Factorization
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Automatic query-based personalized summarization that uses pseudo relevance feedback with NMF
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
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The methods using supervised algorithms for generic document summarization are time-consuming because they need a set training data and associated summaries. We propose a new unsupervised method using the Non-negative Semantic Variable to select the sentences for automatic generic document summarization. The proposed method selects meaningful sentences for generic document summarization. Besides, it can improve the quality of generic summaries because the extracted sentences are well covered with the major topics of document. And also it doesn't need a set training data because it is an unsupervised method. The experimental results demonstrate that the proposed method achieves better performance the other method.