Knowledge-aware methods have boosted a range of Natural Language Processing applications over the last decades. With the gathered momentum, knowledge recently has been pumped into enormous attention in document summarization research. Previous works proved that knowledge-embedded document summarizers excel at generating superior digests, especially in terms of informativeness, coherence, and fact consistency. This paper pursues to present the first systematic survey for the state-of-the-art methodologies that embed knowledge into document summarizers. Particularly, we propose novel taxonomies to recapitulate knowledge and knowledge embeddings under the document summarization view. We further explore how embeddings are generated in learning architectures of document summarization models, especially in deep learning models. At last, we discuss the challenges of this topic and future directions.

翻译：在过去几十年中,知识意识方法推动了一系列自然语言处理应用。随着不断积累的势头,知识最近被注入了文件总结研究的极大关注。以前的工作证明,知识组成的文件摘要员在生成高级文摘方面非常出色,特别是在信息、一致性和事实一致性方面。本文件力求介绍首次系统调查将知识纳入文件摘要的先进方法。特别是,我们提出新的分类法,以重新概括在文件总结观点下的知识和知识嵌入。我们进一步探索如何在文件总结模型的学习结构中,特别是在深层学习模式中,形成嵌入。我们最后讨论了这一专题的挑战和未来的方向。