  Many academic journals ask their authors to provide a list of about five to
fifteen keywords, to appear on the first page of each article. Since these key
words are often phrases of two or more words, we prefer to call them
keyphrases. There is a wide variety of tasks for which keyphrases are useful,
as we discuss in this paper. We approach the problem of automatically
extracting keyphrases from text as a supervised learning task. We treat a
document as a set of phrases, which the learning algorithm must learn to
classify as positive or negative examples of keyphrases. Our first set of
experiments applies the C4.5 decision tree induction algorithm to this learning
task. We evaluate the performance of nine different configurations of C4.5. The
second set of experiments applies the GenEx algorithm to the task. We developed
the GenEx algorithm specifically for automatically extracting keyphrases from
text. The experimental results support the claim that a custom-designed
algorithm (GenEx), incorporating specialized procedural domain knowledge, can
generate better keyphrases than a generalpurpose algorithm (C4.5). Subjective
human evaluation of the keyphrases generated by Extractor suggests that about
80% of the keyphrases are acceptable to human readers. This level of
performance should be satisfactory for a wide variety of applications.
