Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
One story, one flow: Hidden Markov Story Models for multilingual multidocument summarization
ACM Transactions on Speech and Language Processing (TSLP)
Topic-focused multi-document summarization using an approximate oracle score
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Experiments in multidocument summarization
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Mind the gap: dangers of divorcing evaluations of summary content from linguistic quality
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Computational Linguistics
Imitating human literature review writing: an approach to multi-document summarization
ICADL'10 Proceedings of the role of digital libraries in a time of global change, and 12th international conference on Asia-Pacific digital libraries
Nouveau-rouge: A novelty metric for update summarization
Computational Linguistics
Computer Speech and Language
Exploring clustering for multi-document arabic summarisation
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
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Automatic document summarization has become increasingly important due to the quantity of written material generated worldwide. Generating good quality summaries enables users to cope with larger amounts of information. English-document summarization is a difficult task. Yet it is not sufficient. Environmental, economic, and other global issues make it imperative for English speakers to understand how other countries and cultures perceive and react to important events. CLASSY (Clustering, Linguistics, And Statistics for Summarization Yield) is an automatic, extract-generating, summarization system that uses linguistic trimming and statistical methods to generate generic or topic(/query)-driven summaries for single documents or clusters of documents. CLASSY has performed well in the Document Understanding Conference (DUC) evaluations and the Multi-lingual (Arabic/English) Summarization Evaluations (MSE). We present a description of CLASSY. We follow this with experiments and results from the MSE evaluations and conclude with a discussion of on-going work to improve the quality of the summaries-both Englishonly and multi-lingual-that CLASSY generates.