论文标题
知识感知文档摘要：对知识，嵌入方法和体系结构的调查
Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures
论文作者
论文摘要
在过去的几十年中，知识感知的方法促进了一系列自然语言处理应用。随着收集的动力，最近在文档摘要中引起了知识，这是自然语言处理应用之一。先前的作品报告说，知识所限制的文件摘要在产生卓越的消化方面表现出色，尤其是在信息性，连贯性和事实一致性方面。本文追求对将知识嵌入文档摘要中的最新方法论进行的首次系统调查。特别是，我们提出了新颖的分类法，以概括文档摘要观点下的知识和知识嵌入。我们进一步探讨了如何在嵌入文档摘要模型的学习架构时，尤其是深度学习模型的学习架构。最后，我们讨论了这个主题和未来方向的挑战。
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, one of natural language processing applications. Previous works reported 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 embedding learning architectures of document summarization models, especially of deep learning models. At last, we discuss the challenges of this topic and future directions.
