  Withthegrowthofknowledgegraphs, entity descriptions are becoming extremely
lengthy. Entity summarization task, aiming to generate diverse, comprehensive,
and representative summaries for entities, has received increasing interest
recently. In most previous methods, features are usually extracted by the
handcrafted templates. Then the feature selection and multi-user preference
simulation take place, depending too much on human expertise. In this paper, a
novel integration method called AutoSUM is proposed for automatic feature
extraction and multi-user preference simulation to overcome the drawbacks of
previous methods. There are two modules in AutoSUM: extractor and simulator.
The extractor module operates automatic feature extraction based on a BiLSTM
with a combined input representation including word embeddings and graph
embeddings. Meanwhile, the simulator module automates multi-user preference
simulation based on a well-designed two-phase attention mechanism (i.e.,
entity-phase attention and user-phase attention). Experimental results
demonstrate that AutoSUM produces state-of-the-art performance on two widely
used datasets (i.e., DBpedia and LinkedMDB) in both F-measure and MAP.
