The nature of statistical learning theory
The nature of statistical learning theory
A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
The use of unlabeled data to improve supervised learning for text summarization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Cross-lingual C*ST*RD: English access to Hindi information
ACM Transactions on Asian Language Information Processing (TALIP)
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
From single to multi-document summarization: a prototype system and its evaluation
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
Improving multilingual summarization: using redundancy in the input to correct MT errors
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
The Pyramid Method: Incorporating human content selection variation in summarization evaluation
ACM Transactions on Speech and Language Processing (TSLP)
Using Cross-Document Random Walks for Topic-Focused Multi-Document
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Multi-document summarization using cluster-based link analysis
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Extractive summarization using supervised and semi-supervised learning
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
The automatic creation of literature abstracts
IBM Journal of Research and Development
EUSUM: extracting easy-to-understand english summaries for non-native readers
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Using bilingual information for cross-language document summarization
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Summarizing the differences in multilingual news
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Tweet recommendation with graph co-ranking
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Machine translation for multilingual summary content evaluation
Proceedings of Workshop on Evaluation Metrics and System Comparison for Automatic Summarization
Multilingual sentiment analysis using machine translation?
WASSA '12 Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis
Computer Speech and Language
The notion of diversity in graphical entity summarisation on semantic knowledge graphs
Journal of Intelligent Information Systems
| Hi-index | 0.00 |
Cross-language document summarization is a task of producing a summary in one language for a document set in a different language. Existing methods simply use machine translation for document translation or summary translation. However, current machine translation services are far from satisfactory, which results in that the quality of the cross-language summary is usually very poor, both in readability and content. In this paper, we propose to consider the translation quality of each sentence in the English-to-Chinese cross-language summarization process. First, the translation quality of each English sentence in the document set is predicted with the SVM regression method, and then the quality score of each sentence is incorporated into the summarization process. Finally, the English sentences with high translation quality and high informative-ness are selected and translated to form the Chinese summary. Experimental results demonstrate the effectiveness and usefulness of the proposed approach.