The task of cross-language document summarization is to create a summary in a target language from documents in a different source language. Previous methods only involve direct extraction of automatically translated sentences from the original documents. Inspired by phrasebased machine translation, we propose a phrase-based model to simultaneously perform sentence scoring, extraction and compression. We design a greedy algorithm to approximately optimize the score function. Experimental results show that our methods outperform the state-of-theart extractive systems while maintaining similar grammatical quality.
Inspired by the phrase-based translation models, REF introduce a compressive CLS, which simultaneously performs sentence selection and compression.
Phrase-based Compressive Cross-Language Summarization
EMNLP
Computer Science