An efficient domain-independent approach for supervised keyphrase
  extraction and ranking
Sriraghavendra Ramaswamy
We present a supervised learning approach for automatic extraction of
keyphrases from single documents. Our solution uses simple to compute
statistical and positional features of candidate phrases and does not rely on
any external knowledge base or on pre-trained language models or word
embeddings. The ranking component of our proposed solution is a fairly
lightweight ensemble model. Evaluation on benchmark datasets shows that our
approach achieves significantly higher accuracy than several state-of-the-art
baseline models, including all deep learning-based unsupervised models compared
with, and is competitive with some supervised deep learning-based models too.
Despite the supervised nature of our solution, the fact that does not rely on
any corpus of "golden" keywords or any external knowledge corpus means that our
solution bears the advantages of unsupervised solutions to a fair extent.