  Automatic scientific keyphrase extraction is a challenging problem
facilitating several downstream scholarly tasks like search, recommendation,
and ranking. In this paper, we introduce SEAL, a scholarly tool for automatic
keyphrase extraction and classification. The keyphrase extraction module
comprises two-stage neural architecture composed of Bidirectional Long
Short-Term Memory cells augmented with Conditional Random Fields. The
classification module comprises of a Random Forest classifier. We extensively
experiment to showcase the robustness of the system. We evaluate multiple
state-of-the-art baselines and show a significant improvement. The current
system is hosted at http://lingo.iitgn.ac.in:5000/.
