Automatic Topic-oriented Multi-document Summarization with Combination of Query-dependent and Query-independent Rankers

Most up-to-date multi-document summarization systems are built upon the extractive framework, which score and rank the sentences based on the associated features. Generally these features can be classified into two sets: query-dependent features and query-independent features. Query-dependent features are selected for satisfying the topic queries while the query-independent features are for the documents´ focus. In this paper, we propose to build two rankers based SVR model each of which adopts a set of features. Then we design a combination strategy to acquire the sentences which can satisfy both the query focus and the documents´ focus. The evaluations by ROUGE criteria on DUC 2006 and 2007 document sets demonstrate the competability and the adaptability of the proposed approaches.