Text classification using string kernels
The Journal of Machine Learning Research
The Journal of Machine Learning Research
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A unified framework for automatic evaluation using N-gram co-occurrence statistics
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Dependency-based sentence alignment for multiple document summarization
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Corpus and evaluation measures for multiple document summarization with multiple sources
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
An automatic method for summary evaluation using multiple evaluation results by a manual method
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Supervised automatic evaluation for summarization with voted regression model
Information Processing and Management: an International Journal
Corroborating text evaluation results with heterogeneous measures
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The heterogeneity principle in evaluation measures for automatic summarization
Proceedings of Workshop on Evaluation Metrics and System Comparison for Automatic Summarization
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In order to promote the study of automatic summarization and translation, we need an accurate automatic evaluation method that is close to human evaluation. In this paper, we present an evaluation method that is based on convolution kernels that measure the similarities between texts considering their substructures. We conducted an experiment using automatic summarization evaluation data developed for Text Summarization Challenge 3 (TSC-3). A comparison with conventional techniques shows that our method correlates more closely with human evaluations and is more robust.