  Keyphrases provide semantic metadata that summarize and characterize
documents. This paper describes Kea, an algorithm for automatically extracting
keyphrases from text. Kea identifies candidate keyphrases using lexical
methods, calculates feature values for each candidate, and uses a
machine-learning algorithm to predict which candidates are good keyphrases. The
machine learning scheme first builds a prediction model using training
documents with known keyphrases, and then uses the model to find keyphrases in
new documents. We use a large test corpus to evaluate Kea's effectiveness in
terms of how many author-assigned keyphrases are correctly identified. The
system is simple, robust, and publicly available.
