A Supervised Aggregation Framework for Multi-Document Summarization

In most summarization approaches, sentence ranking plays a vital role. Most previous work explored different features and combined them into unified ranking methods. However, it would be imprecise to rank sentences from a single point of view because contributions from the features are onefold in these methods. In this paper, a novel supervised aggregation approach for summarization is proposed which combines different summarization methods including LexPageRank, LexHITS, manifold-ranking method and DivRank. Human labeled data are used to train an optimization model which combines these multiple summarizers and then the weights assigned to each individual summarizer are learned. Experiments are conducted on DUC2004 data set and the results demonstrate the effectiveness of the supervised aggregation method compared with typical ensemble approaches. In addition, we also investigate the influence of training data construction and component diversity on the summarization results.