GuideRank: A Guided Ranking Graph Model for Multilingual Multi-document Summarization
Multilingual multi-document summarization is a task to generate the summary in target language from a collection of documents in multiple source languages.A straightforward approach to this task is automatically translating the non-target language documents into target language and then applying monolingual summarization methods,but the summaries generated by this method is often poorly readable due to the low quality of machine translation.To solve this problem,we propose a novel graph model based on guided edge weighting method in which both informativeness and readability of summaries are taken into consideration fully.In methodology,our model attempts to choose from the target language documents the sentences which contain important shared information across languages,and also retains the salient sentences which cannot be covered by documents in other language.The experimental results on our manually labeled dataset 1 show that our method signi cantly outperforms other baseline methods.
Haoran Li Jiajun Zhang Yu Zhou Chengqing Zong
National Laboratory of Pattern Recognition,Institute of Automation Chinese Academy of Sciences,Beiji University of Chinese Academy of Sciences,Beijing,China
国际会议
第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)
昆明
英文
1-12
2016-12-02（万方平台首次上网日期，不代表论文的发表时间）