A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Generating summaries of multiple news articles
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic text structuring and summarization
Information Processing and Management: an International Journal - Special issue: methods and tools for the automatic construction of hypertext
Training a selection function for extraction
Proceedings of the eighth international conference on Information and knowledge management
Data mining: concepts and techniques
Data mining: concepts and techniques
Korean text summarization using an aggregate similarity
IRAL '00 Proceedings of the fifth international workshop on on Information retrieval with Asian languages
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
Information Retrieval Systems: Theory and Implementation
Information Retrieval Systems: Theory and Implementation
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
A novel word clustering algorithm based on latent semantic analysis
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Using coreference chains for text summarization
CorefApp '99 Proceedings of the Workshop on Coreference and its Applications
Text summarization using a trainable summarizer and latent semantic analysis
Information Processing and Management: an International Journal - Special issue: An Asian digital libraries perspective
GA, MR, FFNN, PNN and GMM based models for automatic text summarization
Computer Speech and Language
How evolutionary algorithms are applied to statistical natural language processing
Artificial Intelligence Review
Automatic text summarization based on latent semantic indexing
Artificial Life and Robotics
Ubiquitous Healthcare Service System with Context-awareness Capability: Design and Implementation
Expert Systems with Applications: An International Journal
Digital library development in the asia pacific
ICADL'05 Proceedings of the 8th international conference on Asian Digital Libraries: implementing strategies and sharing experiences
Expert Systems with Applications: An International Journal
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In this paper, two novel approaches are proposed to extract important sentences from a document to create its summary. The first is a corpus-based approach using feature analysis. It brings up three new ideas: 1) to employ ranked position to emphasize the significance of sentence position, 2) to reshape word unit to achieve higher accuracy of keyword importance, and 3) to train a score function by the genetic algorithm for obtaining a suitable combination of feature weights. The second approach combines the ideas of latent semantic analysis and text relationship maps to interpret conceptual structures of a document. Both approaches are applied to Chinese text summarization. The two approaches were evaluated by using a data corpus composed of 100 articles about politics from New Taiwan Weekly, and when the compression ratio was 30%, average recalls of 52.0% and 45.6% were achieved respectively.