RankUp: Enhancing graph-based keyphrase extraction methods with error-feedback propagation. (January 2018) - Figueroa, Gerardo; Chen, Po-Chi; Chen, Yi-Shin
Highlights: An error-feedback algorithm for graph-based keyword extraction methods is proposed. Backpropagation increases the quality of extracted keyphrases in short articles. Term-weighting measures can be utilized to calculate expected keyphrase scores. Method performs better than the baseline methods and term-weighting approaches. Abstract: In recent years, unsupervised, graph-based ranking algorithms have been successfully applied to keyphrase extraction tasks. These methods have the advantage of taking into account global information, such as text structure and relations between words, phrases, and sentences, rather than relying solely on local, vertex-specific information. Graph-based approaches for keyphrase extraction, however, have a particular drawback, which comes from their frequency-based analysis methods. The weakness is that many common, less relevant terms may get a higher ranking, particularly in short articles. The converse situation also occurs, where less common (and possibly more relevant) terms obtain lower rankings. We propose an unsupervised method—RankUp—that enhances graph-based keyphrase extraction approaches by applying an error-feedback mechanism similar to the concept of backpropagation. Experiments have been performed on almost 3, 300 short texts from a variety of domains. Our experiments show that error-feedback propagation can boost the quality of keyphrases in graph-based keyphrase extraction techniques.