An Exploration of Document Impact on Graph-Based Multi-Document Summarization

The graph-based ranking algorithm has been recently exploited for multi-document summarization by making only use of the sentence-to-sentence relationships in the documents, under the assumption that all the sentences are indistinguishable. However, given a document set to be summarized, different documents are usually not equally important, and moreover, different sentences in a specific document are usually differently important. This paper aims to explore document impact on summarization performance. We propose a document-based graph model to incorporate the document-level information and the sentence-to-document relationship into the graph-based ranking process. Various methods are employed to evaluate the two factors. Experimental results on the DUC2001 and DUC2002 datasets demonstrate that the good effectiveness of the proposed model. Moreover, the results show the robustness of the proposed model.