  Entity summarization has been a prominent task over knowledge graphs. While
existing methods are mainly unsupervised, we present DeepLENS, a simple yet
effective deep learning model where we exploit textual semantics for encoding
triples and we score each candidate triple based on its interdependence on
other triples. DeepLENS significantly outperformed existing methods on a public
benchmark.
