The Algorithm of Automatic Text Summarization Based on Network Representation Learning.

The graph models are an important method in automatic text summarization. However, there will be problems of vector sparseness and information redundancy in text map to graph. In this paper, we propose a graph clustering summarization algorithm based on network representation learning. The sentences graph was construed by TF-IDF, and controlled the number of edges by a threshold. The Node2Vec is used to embedding the graph, and the sentences were clustered by k-means. Finally, the Modularity is used to control the number of clusters, and generating a brief summary of the document. The experiments on the MultiLing 2013 show the proposed algorithm improves the F-Score in ROUGE-1 and ROUGE-2.