A new quantitative quality measure for machine translation systems
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Applications of automatic evaluation methods to measuring a capability of speech translation system
EACL '03 Proceedings of the tenth conference on European chapter of the Association for 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
A comparison of rankings produced by summarization evaluation measures
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLPWorkshop on Automatic summarization - Volume 4
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Expert Systems with Applications: An International Journal
Intelligent location-based mobile news service system with automatic news summarization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Automatic evaluation of texts by using paraphrases
LTC'09 Proceedings of the 4th conference on Human language technology: challenges for computer science and linguistics
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To solve a problem of how to evaluate computer-produced summaries, a number of automatic and manual methods have been proposed. Manual methods evaluate summaries correctly, because humans evaluate them, but are costly. On the other hand, automatic methods, which use evaluation tools or programs, are low cost, although these methods cannot evaluate summaries as accurately as manual methods. In this paper, we investigate an automatic evaluation method that can reduce the errors of traditional automatic methods by using several evaluation results obtained manually. We conducted some experiments using the data of the Text Summarization Challenge 2 (TSC-2). A comparison with conventional automatic methods shows that our method outperforms other methods usually used.