GuideRank: A guided ranking graph model for multilingual multi-document summarization

Abstract

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 (It will be released to the public.) show that our method significantly outperforms other baseline methods.