This paper presents a study on if and how automatically extracted
keywords can be used to improve text categorization. In summary we
show that a higher performance --- as measured by micro-averaged
F-measure on a standard text categorization collection --- is achieved
when the full-text representation is combined with the automatically
extracted keywords. The combination is obtained by giving higher
weights to words in the full-texts that are also extracted as
keywords. We also present results for experiments in which the
keywords are the only input to the categorizer, either represented as
unigrams or intact. Of these two experiments, the unigrams have the
best performance, although neither performs as well as headlines only.