Multi-View Sentence Ranking for Query-Biased Summarization

This paper proposes a novel sentence ranking approach to query-biased summarization where ranking performance can be boosted by encouraging biased information richness in a multi-view framework. To investigate how the final ranking result can benefit from diverse local ranking´s combination, the proposed approach firstly constructs two base rankers to rank all the sentences in a document set from two independent but complementary views (i.e. query-dependent view and query-independent view), and then aggregates them into a consensus one, which can overcome each base ranker´s local preference and improve global robustness of the overall ranking result. Experimental results on the DUC dataset illustrate the effectiveness of the proposed method.