  Keyphrases provide an extremely dense summary of a text. Such information can
be used in many Natural Language Processing tasks, such as information
retrieval and text summarization. Since previous studies on Persian keyword or
keyphrase extraction have not published their data, the field suffers from the
lack of a human extracted keyphrase dataset. In this paper, we introduce
PerKey, a corpus of 553k news articles from six Persian news websites and
agencies with relatively high quality author extracted keyphrases, which is
then filtered and cleaned to achieve higher quality keyphrases. The resulted
data was put into human assessment to ensure the quality of the keyphrases. We
also measured the performance of different supervised and unsupervised
techniques, e.g. TFIDF, MultipartiteRank, KEA, etc. on the dataset using
precision, recall, and F1-score.
