Abstract：Proper processing of the document set based on its semantic structure helps bring about better multi-document summaries. In this paper, subject-object-predicate triples are firstly extracted from document set to construct document semantic graph. Then the edit distance-based clustering and PageRank algorithm are applied to optimize the graph structure and to assign weights to the vertices and links, respectively. Finally, triples with more weighted vertices and links are collected as the summary. Evaluated against the extraction-based summarization in terms of the ROUGE score on a set of manual generated summaries, it shows that the semantic graph-based summarization gained more overlaps with manually created summaries, and the edit distance-based graph structure optimization is positive to the the summarization quality. Key words computer application; Chinese information processing; document semantic graph; edit distance; Page-Rank; ROUGE; Chinese multi-document summarization
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2009, Vol. 23 
