Abstract
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.
Cite
CITATION STYLE
Mihalcea, R., & Tarau, P. (2004). TextRank: Bringing order into texts. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, EMNLP 2004 - A meeting of SIGDAT, a Special Interest Group of the ACL held in conjunction with ACL 2004 (pp. 404–411). Association for Computational Linguistics (ACL).
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