TSGVi: a graph-based summarization system for Vietnamese documents
This paper proposes an automatic method to generate an extractive summary of multiple Vietnamese documents which are related to a common topic by modeling text documents as weighted undirected graphs. It initially builds undirected graphs with vertices representing the sentences of documents and edges indicate the similarity between sentences. Then, by adopting PageRank algorithm, we can generate salient scores for sentences. Sentences are ranked according to their salient scores and selected based on maximal marginal relevance to form the summaries. These summaries are combined and applied the same process one more time to form the final extractive summary of the document set. A series of experiments are performed on Vietnamese news articles and English data of DUC 2002, 2003, 2007. The results demonstrate the effectiveness of the proposed technique over reference systems.
vietnamese graph model weighted pagerank sentence extraction multi-document summarization