Entity Summarization: State of the Art and Future Challenges

Liu, Qingxia; Cheng, Gong; Gunaratna, Kalpa; Qu, Yuzhong

doi:10.2139/ssrn.3944540

Cited by 6 publications

(10 citation statements)

References 32 publications

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“…Methods for Entity Summarization. In a recent survey [15] we have categorized the broad spectrum of research on entity summarization. Below we briefly review general-purpose entity summarizers which mainly rely on generic technical features that can apply to a wide range of domains and applications.…”

Section: Related Workmentioning

confidence: 99%

“…A user served with all of those triples may suffer information overload and find it difficult to quickly identify the small set of triples that are truly needed. To solve the problem, an established research topic is entity summarization [15], which aims to compute an optimal compact summary for the entity by selecting a size-constrained subset of triples. An example entity summary under the size constraint of 5 triples is shown in the bottom right corner of Fig.…”

Section: Introductionmentioning

confidence: 99%

“…Entity summaries constitute entity cards displayed in search engines [9], provide background knowledge for enriching documents [26], and facilitate research activities with humans in the loop [3,4]. This far-reaching application has led to fruitful research as reviewed in our recent survey paper [15]. Many entity summarizers have been developed, most of which generate summaries for general purposes.…”

Section: Introductionmentioning

confidence: 99%

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ESBM: An Entity Summarization BenchMark

Liu¹,

Cheng²,

Gunaratna³

et al. 2020

Preprint

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Entity summarization is the problem of computing an optimal compact summary for an entity by selecting a size-constrained subset of triples from RDF data. Entity summarization supports a multiplicity of applications and has led to fruitful research. However, there is a lack of evaluation efforts that cover the broad spectrum of existing systems. One reason is a lack of benchmarks for evaluation. Some benchmarks are no longer available, while others are small and have limitations. In this paper, we create an Entity Summarization BenchMark (ESBM) which overcomes the limitations of existing benchmarks and meets standard desiderata for a benchmark. Using this largest available benchmark for evaluating general-purpose entity summarizers, we perform the most extensive experiment to date where 9 existing systems are compared. Considering that all of these systems are unsupervised, we also implement and evaluate a supervised learning based system for reference.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ESBM: An Entity Summarization BenchMark

Liu¹,

Cheng²,

Gunaratna³

et al. 2020

Preprint

Self Cite

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“…Entity summarization is the task of computing a compact summary for an entity by selecting an optimal size-constrained subset of entity-property-value triples from a knowledge graph such as an RDF graph [7]. It has found a wide variety of applications, for example, to generate a compact entity card from Google's Knowledge Graph where an entity may be described in dozens or hundreds of triples.…”

Section: Introductionmentioning

confidence: 99%

DeepLENS: Deep Learning for Entity Summarization

Liu¹,

Cheng²,

Qu³

2020

Preprint

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“…Since Google first released the knowledge graph, "get the best summary" for entities has been one of the main contributions in Google Search 4 [25]. Specifically, Google Search returns a top-k subset of triples which can best describe the entity from a query on the right-hand side of the result pages [15]. Motivated by the success of Google Search, entity summarization task has received an increasing interest recently [7,25], it aims to generate diverse, comprehensive and representative summaries for entities.…”

Section: Introductionmentioning

confidence: 99%

AutoSUM: Automating Feature Extraction and Multi-user Preference Simulation for Entity Summarization

Wei¹,

Liu²,

Zhu³

et al. 2020

Preprint

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With the growth of knowledge graphs, entity descriptions are becoming extremely lengthy. Entity summarization task, aiming to generate diverse, comprehensive and representative summaries for entities, has received an increasing interest recently. In most previous methods, features are usually extracted by the hand-crafted 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 twophase attention mechanism (i.e., entity-phase attention and user-phase attention). Experimental results demonstrate that AutoSUM produces the state-of-the-art performance on two widely used datasets (i.e., DBpedia and LinkedMDB) in both F-measure and MAP. The source code and outputs are available at https://github.com/WeiDongjunGabriel/ AutoSUM.

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Entity Summarization: State of the Art and Future Challenges

Cited by 6 publications

References 32 publications

ESBM: An Entity Summarization BenchMark

ESBM: An Entity Summarization BenchMark

DeepLENS: Deep Learning for Entity Summarization

AutoSUM: Automating Feature Extraction and Multi-user Preference Simulation for Entity Summarization

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