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. In particular, 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.
Mihalcea REF had discussed the use of TextRank -a graph-based ranking model for text processing.
TextRank: Bringing Order Into Texts
SIGDAT Conference On Empirical Methods In Natural Language Processing
Computer Science