  The increasing availability of semantic data has substantially enhanced Web
applications. Semantic data such as RDF data is commonly represented as
entity-property-value triples. The magnitude of semantic data, in particular
the large number of triples describing an entity, could overload users with
excessive amounts of information. This has motivated fruitful research on
automated generation of summaries for entity descriptions to satisfy users'
information needs efficiently and effectively. We focus on this prominent topic
of entity summarization, and our research objective is to present the first
comprehensive survey of entity summarization research. Rather than separately
reviewing each method, our contributions include (1) identifying and
classifying technical features of existing methods to form a high-level
overview, (2) identifying and classifying frameworks for combining multiple
technical features adopted by existing methods, (3) collecting known benchmarks
for intrinsic evaluation and efforts for extrinsic evaluation, and (4)
suggesting research directions for future work. By investigating the
literature, we synthesized two hierarchies of techniques. The first hierarchy
categories generic technical features into several perspectives: frequency and
centrality, informativeness, and diversity and coverage. In the second
hierarchy we present domain-specific and task-specific technical features,
including the use of domain knowledge, context awareness, and personalization.
Our review demonstrated that existing methods are mainly unsupervised and they
combine multiple technical features using various frameworks: random surfer
models, similarity-based grouping, MMR-like re-ranking, or combinatorial
optimization. We also found a few deep learning based methods in recent
research.
