A trainable document summarizer
SIGIR '95 Proceedings of the 18th 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
Cut and paste based text summarization
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Abstract generation based on rhetorical structure extraction
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Evaluation challenges in large-scale document summarization
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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IBM Journal of Research and Development
Improving LSA-based summarization with anaphora resolution
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Two uses of anaphora resolution in summarization
Information Processing and Management: an International Journal
Update Summarization Based on Latent Semantic Analysis
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
Using parallel corpora for multilingual (multi-document) summarisation evaluation
CLEF'10 Proceedings of the 2010 international conference on Multilingual and multimodal information access evaluation: cross-language evaluation forum
Recent advances in automatic speech summarization
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Challenges and solutions in the opinion summarization of user-generated content
Journal of Intelligent Information Systems
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Proceedings of Workshop on Evaluation Metrics and System Comparison for Automatic Summarization
Tailoring trusted semantic information
Proceedings of the Eighth Annual Cyber Security and Information Intelligence Research Workshop
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In this paper we present the usage of singular value decomposition (SVD) in text summarization. Firstly, we mention the taxonomy of generic text summarization methods. Then we describe principles of the SVD and its possibilities to identify semantically important parts of a text. We propose a modification of the SVD-based summarization, which improves the quality of generated extracts. In the second part we propose two new evaluation methods based on SVD, which measure content similarity between an original document and its summary. In evaluation part, our summarization approach is compared with 5 other available summarizers. For evaluation of a summary quality we used, apart from a classical content-based evaluator, both newly developed SVD-based evaluators. Finally, we study the influence of the summary length on its quality from the angle of the three evaluation methods mentioned.