论文标题
从长文档中提取摘要知识图
Extracting Summary Knowledge Graphs from Long Documents
论文作者
论文摘要
知识图从长文档捕获实体和关系，并可以在许多下游应用程序中促进推理。提取仅包含显着实体和关系的紧凑知识图对于理解和总结长文件很重要，但具有挑战性。我们介绍了一项新的文本到图表任务，以预测长文档的汇总知识图。我们使用自动和人类注释开发了200K文档/图对的数据集。我们还基于图形学习和文本摘要为此任务开发了强大的基准，并提供了对其效果的定量和定性研究。
Knowledge graphs capture entities and relations from long documents and can facilitate reasoning in many downstream applications. Extracting compact knowledge graphs containing only salient entities and relations is important but challenging for understanding and summarizing long documents. We introduce a new text-to-graph task of predicting summarized knowledge graphs from long documents. We develop a dataset of 200k document/graph pairs using automatic and human annotations. We also develop strong baselines for this task based on graph learning and text summarization, and provide quantitative and qualitative studies of their effect.
