Most existing learning to rank based summarization methods only used content relevance of sentences with respect to queries to rank or estimate sentences, while neglecting sentence relationships. In our work, we propose a novel model, RelationListwise, by integrating relation information among all the estimated sentences into listMLE-Top K, a basic listwise learning to rank model, to improve the quality of top-ranked sentences. In addition, we present some unique sentence features as well as a novel measure of sentence semantic relation, aiming to enhance the performance of training model. Experimental results on DUC2005-2007 standard summarization data sets demonstrate the effectiveness of our proposed method.
['Wenpeng Yin', 'Lifu Huang', 'Yulong Pei', "Lian'en Huang"]
RelationListwise for Query-Focused Multi-Document Summarization