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Entity summarization aims at creating brief but informative descriptions of entities from knowledge graphs. While previous work mostly focused on traditional techniques such as clustering algorithms and graph models, we ask how to apply deep learning methods into this task. In this paper we propose ESA, a neural network with supervised attention mechanisms for entity summarization. Specifically, we calculate attention weights for facts in each entity, and rank facts to generate reliable summaries. We explore techniques to solve difficult learning problems presented by the ESA, and demonstrate the effectiveness of our model in comparison with the state-of-the-art methods. Experimental results show that our model improves the quality of the entity summaries in both F-measure and MAP.
A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document. For example, state-of-the-art models trained on existing datasets exhibit entity hallucination, generating n
Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes
Entity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer. The proposed model treats words and
In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts. Many applications require abridge
Recent progress of abstractive text summarization largely relies on large pre-trained sequence-to-sequence Transformer models, which are computationally expensive. This paper aims to distill these large models into smaller ones for faster inference a