论文标题
神经实体摘要与联合编码和弱监督
Neural Entity Summarization with Joint Encoding and Weak Supervision
论文作者
论文摘要
在大规模知识图（kg）中，实体通常由大量三结构化事实描述。许多应用程序需要简化的实体描述版本，称为实体摘要。现有的实体摘要解决方案主要是无监督的。在本文中，我们提出了一种基于我们新颖的神经模型的监督方法巢，以共同编码kgs中的图形结构和文本，并产生高质量的多元化摘要。由于获得手动标记的培训摘要是昂贵的，因此我们的监督很弱，因为我们使用编程标记的数据进行培训，这些数据可能包含噪声，但没有手动工作。评估结果表明，我们的方法在两个公共基准上大大优于最新技术。
In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts. Many applications require abridged versions of entity descriptions, called entity summaries. Existing solutions to entity summarization are mainly unsupervised. In this paper, we present a supervised approach NEST that is based on our novel neural model to jointly encode graph structure and text in KGs and generate high-quality diversified summaries. Since it is costly to obtain manually labeled summaries for training, our supervision is weak as we train with programmatically labeled data which may contain noise but is free of manual work. Evaluation results show that our approach significantly outperforms the state of the art on two public benchmarks.
