Cross-Language Document Summarization Based on Machine Translation Quality Prediction

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 917–926, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Cross-Language Document Summarization Based on Machine Translation Quality Prediction Xiaojun Wan, Huiying Li and Jianguo Xiao Institute of Compute Science and Technology, Peking University, Beijing 100871, China Key Laboratory of Computational Linguistics (Peking University), MOE, China {wanxiaojun,lihuiying,xiaojianguo}@icst.pku.edu.cn Abstract Cross-language document summarization is a task of producing a summary in one language for a document set in a different language. Ex- isting methods simply use machine translation for document translation or summary transla- tion. However, current machine translation services are far from satisfactory, which re- sults in that the quality of the cross-language summary is usually very poor, both in read- ability and content. In this paper, we propose to consider the translation quality of each sen- tence in the English-to-Chinese cross-language summarization process. First, the translation quality of each English sentence in the docu- ment set is predicted with the SVM regression method, and then the quality score of each sen- tence 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 dem- onstrate the effectiveness and usefulness of the proposed approach. 1 Introduction Given a document or document set in one source language, cross-language document summariza- tion aims to produce a summary in a different target language. In this study, we focus on Eng- lish-to-Chinese document summarization for the purpose of helping Chinese readers to quickly understand the major content of an English docu- ment or document set. This task is very impor- tant in the field of multilingual information ac- cess. Till now, most previous work focuses on monolingual document summarization, but cross-language document summarization has re- ceived little attention in the past years. A straightforward way for cross-language docu- ment summarization is to translate the summary from the source language to the target language by using machine translation services. However, though machine translation techniques have been advanced a lot, the machine translation quality is far from satisfactory, and in many cases, the translated texts are hard to understand. Therefore, the translated summary is likely to be hard to understand by readers, i.e., the summary quality is likely to be very poor. For example, the trans- lated Chinese sentence for an ordinary English sentence (“It is also Mr Baker who is making the most of presidential powers to dispense lar- gesse.”) by using Google Translate is “同时，也 是贝克是谁提出了对总统权力免除最慷慨。”. The translated sentence is hard to understand because it contains incorrect translations and it is very disfluent. If such sentences are selected into the summary, the quality of the summary would be very poor. In order to address the above problem, we propose to consider the translation quality of the English sentences in the summarization process. In particular, the translation quality of each Eng- lish sentence is predicted by using the SVM re- gression method, and then …

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