  Concept extraction is crucial for a number of downstream applications.
However, surprisingly enough, straightforward single token/nominal
chunk-concept alignment or dictionary lookup techniques such as DBpedia
Spotlight still prevail. We propose a generic open-domain OOV-oriented
extractive model that is based on distant supervision of a pointer-generator
network leveraging bidirectional LSTMs and a copy mechanism. The model has been
trained on a large annotated corpus compiled specifically for this task from
250K Wikipedia pages, and tested on regular pages, where the pointers to other
pages are considered as ground truth concepts. The outcome of the experiments
shows that our model significantly outperforms standard techniques and, when
used on top of DBpedia Spotlight, further improves its performance. The
experiments furthermore show that the model can be readily ported to other
datasets on which it equally achieves a state-of-the-art performance.
