C4.5: programs for machine learning
C4.5: programs for machine learning
A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Stochastic complexity in learning
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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
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
New Methods in Automatic Extracting
Journal of the ACM (JACM)
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
Evaluating Natural Language Processing Systems: An Analysis and Review
Evaluating Natural Language Processing Systems: An Analysis and Review
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distribution of content words and phrases in text and language modelling
Natural Language Engineering
The rhetorical parsing of natural language texts
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Fast generation of abstracts from general domain text corpora by extracting relevant sentences
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Query-relevant summarization using FAQs
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLPWorkshop on Automatic summarization - Volume 4
Supervised ranking in open-domain text summarization
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Summary of FAQs from a topical forum based on the native composition structure
Expert Systems with Applications: An International Journal
Applied Computational Intelligence and Soft Computing
Information Processing and Management: an International Journal
MCMR: Maximum coverage and minimum redundant text summarization model
Expert Systems with Applications: An International Journal
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The paper introduces a novel approach to unsupervised text summarization, which in principle should work for any domain or genre. The novelty lies in exploiting the diversity of concepts in text for summarization, which has not received much attention in the summarization literature. We propose, in addition, what we call the information-centric approach to evaluation, where the quality of summaries is judged not in terms of how well they match human-created summaries but in terms of how well they represent their source documents in IR tasks such document retrieval and text categorization. To find the effectiveness of our approach under the proposed evaluation scheme, we set out to examine how a system with the diversity functionality performs against one without, using the test data known as BMIR-J2. The results demonstrate a clear superiority of the diversity-based approach to a non-diversity-based approach.The paper also addresses the question of how closely the diversity approach models human judgments on summarization. We have created a relatively large volume of data annotated for relevance to summarization by human subjects. We have trained a decision tree-based summarizer using the data, and examined how the diversity method compares with the supervised method in performance when tested on the data. It was found that the diversity approach performs as well as and in some cases superior to the supervised method.