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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	AutoSUM: Automating Feature Extraction and Multi-user Preference Simulation for Entity Summarization
Creator	Zhou, Wei Hu, Songlin Liu, Yaxin Lu, Yijun Wei, Dongjun Zang, Liangjun Zhu, Fuqing
topic	covid:9157db1db52832f92a408dbb267004f9ac057996#this
Source	PMC
abstract	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 two-phase 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.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-47436-2_44
has license	no-cc
sha1sum (hex)	9157db1db52832f92a408dbb267004f9ac057996
schema:url	https://doi.org/10.1007/978-3-030-47436-2_44
resource representing a document's title	AutoSUM: Automating Feature Extraction and Multi-user Preference Simulation for Entity Summarization
has PubMed Central identifier	PMC7206294
schema:publication	Advances in Knowledge Discovery and Data Mining
resource representing a document's body	covid:9157db1db52832f92a408dbb267004f9ac057996#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/9157db1db52832f92a408dbb267004f9ac057996
is schema:about of	named entity 'human' named entity 'simulation' named entity 'module' named entity 'multi-user' named entity 'automatic' named entity 'There' named entity 'representation' named entity 'Simulation' named entity 'Entity' named entity 'Preference' named entity 'USUALLY' named entity 'EXPERIMENTAL RESULTS' named entity 'ART' named entity 'REPRESENTATION' named entity 'WORD' named entity 'PREVIOUS' covid:arg/9157db1db52832f92a408dbb267004f9ac057996 named entity 'generate' named entity 'datasets' named entity 'demonstrate' named entity 'ESA' named entity 'Meanwhile' named entity 'extractor' named entity 'feature extraction' named entity 'LDA' named entity 'triples' named entity 'feature extraction' named entity 'feature extraction' named entity 'extracted features' named entity 'knowledge graph' named entity 'triples' named entity 'knowledge graph' named entity 'entity recognition' named entity 'score function' named entity 'ESA' named entity 'score function' named entity 'predicates' named entity 'structured data' named entity 'NER' named entity 'word embeddings' named entity 'Google Search' named entity 'NER' named entity 'cross-validation' named entity 'paired t-test' named entity 'F-measure' named entity 'Graph Embedding' named entity 'Deep Learning' named entity 'predicates' named entity 'ESA' named entity 'conditional random fields' named entity 'triples' named entity 'Feature Extraction' named entity 'deep learning' named entity 'LSTM' named entity 'Latent Dirichlet Allocation' named entity 'ablation studies' named entity 'MAP' named entity 'triples' named entity 'loss function' named entity 'word embedding' named entity 'LSTM' named entity 'F-measure' named entity 'triples' named entity 'F-measure' named entity 'LDA' named entity 'subset' named entity 'Google Search' named entity 'feature extraction' named entity 'feature extraction' named entity 'triples' named entity 'LSTM' named entity 'recall and precision' named entity 'triples' named entity 'knowledge graphs' named entity 'word embeddings'

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