In this paper, we introduce TextRank-a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications.
we propose two innovative unsupervised methods for keyword and sentence extraction, and show that the results obtained compare favorably with previously published results on established benchmarks.