  Open Information Extraction (OpenIE) facilitates domain-independent discovery
of relational facts from large corpora. The technique well suits many
open-world natural language understanding scenarios, such as automatic
knowledge base construction, open-domain question answering, and explicit
reasoning. Thanks to the rapid development in deep learning technologies,
numerous neural OpenIE architectures have been proposed and achieve
considerable performance improvement. In this survey, we provide an extensive
overview of the-state-of-the-art neural OpenIE models, their key design
decisions, strengths and weakness. Then, we discuss limitations of current
solutions and the open issues in OpenIE problem itself. Finally we list recent
trends that could help expand its scope and applicability, setting up promising
directions for future research in OpenIE. To our best knowledge, this paper is
the first review on this specific topic.
