As a fundamental and effective tool for document understanding and organization, multi-document
summarization enables better information services by creating concise and informative reports for large collections
of documents. In this paper, we propose a sentence-word two layer graph algorithm combining with keyword
density to generate the multi-document summarization, known as Graph & Keywordρ. The traditional graph
methods of multi-document summarization only consider the influence of sentence and word in all documents
rather than individual documents. Therefore, we construct multiple word graph and extract right keywords
in each document to modify the sentence graph and to improve the significance and richness of the summary.
Meanwhile, because of the differences in the words importance in documents, we propose to use keyword density
for the summaries to provide rich content while using a small number of words. The experiment results show that
the Graph & Keywordρ method outperforms the state of the art systems when tested on the Duc2004 data set.
YE Feiyue (叶飞跃), XU Xinchen (徐欣辰)
. Automatic Multi-Document Summarization Based on Keyword Density and Sentence-Word Graphs[J]. Journal of Shanghai Jiaotong University(Science), 2018
, 23(4)
: 584
.
DOI: 10.1007/s12204-018-1957-2
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