  Open Information Extraction (OpenIE) facilitates the open-domain discovery of
textual facts. However, the prevailing solutions evaluate OpenIE models on
in-domain test sets aside from the training corpus, which certainly violates
the initial task principle of domain-independence. In this paper, we propose to
advance OpenIE towards a more realistic scenario: generalizing over unseen
target domains with different data distributions from the source training
domains, termed Generalized OpenIE. For this purpose, we first introduce GLOBE,
a large-scale human-annotated multi-domain OpenIE benchmark, to examine the
robustness of recent OpenIE models to domain shifts, and the relative
performance degradation of up to 70% implies the challenges of generalized
OpenIE. Then, we propose DragonIE, which explores a minimalist graph expression
of textual fact: directed acyclic graph, to improve the OpenIE generalization.
Extensive experiments demonstrate that DragonIE beats the previous methods in
both in-domain and out-of-domain settings by as much as 6.0% in F1 score
absolutely, but there is still ample room for improvement.
