  The SemEval-2010 benchmark dataset has brought renewed attention to the task
of automatic keyphrase extraction. This dataset is made up of scientific
articles that were automatically converted from PDF format to plain text and
thus require careful preprocessing so that irrevelant spans of text do not
negatively affect keyphrase extraction performance. In previous work, a wide
range of document preprocessing techniques were described but their impact on
the overall performance of keyphrase extraction models is still unexplored.
Here, we re-assess the performance of several keyphrase extraction models and
measure their robustness against increasingly sophisticated levels of document
preprocessing.
