Comparative Document Summarization via Discriminative Sentence Selection

Abstract—Cross-language multidocument summarization is the task to generate a summary in a target language (e.g., Chinese) from a collection of documents in a different source language (e.g., English). Previous methods such as the extractive and compressive algorithms focus only on single sentence selection and compression, which cannot make full use of the similar sentences containing complementary information. Furthermore, the translation model knowledge is not fully explored in previous approaches. To address these two problems, we propose in this paper an abstractive cross-language summarization framework. First, the source language documents are translated into target language with a machine translation system. Then, the method constructs a pool of bilingual concepts and facts represented by the bilingual elements of the source-side predicate-argument structures (PAS) and their target-side counterparts. Finally, < Final Year Projects > new summary sentences are produced by fusing bilingual PAS elements with the integer linear programming algorithm to maximize both of the salience and translation quality of the PAS elements. The experimental results on English-to-Chinese cross-language summarization demonstrate that our proposed method outperforms the state-of-the-art extractive systems in both automatic and manual evaluations.