Abstract
Traditionally, keyphrases (or keywords) have been manually assigned to documents by their authors or by human indexers. This, however, has become impractical due to the massive growth of documents on the Internet each day, thus creating a need for systems that automatically extract keyphrases from documents. Automatic keyphrase extraction methods have generally taken either supervised or unsupervised approaches. 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. First, 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. Second, we present a hybrid keyphrase extraction method for short articles, HybridRank, which leverages the benefits of both supervised and unsupervised approaches. Our system implements modified versions of the TextRank (unsupervised) and KEA (supervised) methods, and applies a merging algorithm to produce an overall list of keyphrases. We have tested HybridRank on more than 900 abstracts belonging to a wide variety of subjects, including engineering, science, physics and IT, and show its superior effectiveness. It is observed that knowledge collaboration between supervised and unsupervised methods can produce higher-quality keyphrases than applying these methods individually.