AutoSUM: Automating Feature Extraction and Multi-user Preference Simulation for Entity Summarization
This work addresses the need for efficient entity summarization in knowledge graphs, which is crucial for users dealing with large-scale data, though it is an incremental improvement over existing methods.
The paper tackles the problem of generating diverse and representative entity summaries from lengthy knowledge graphs by automating feature extraction and multi-user preference simulation, achieving state-of-the-art performance on DBpedia and LinkedMDB datasets in F-measure and MAP metrics.
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.