  A major drawback of modern neural OpenIE systems and benchmarks is that they
prioritize high coverage of information in extractions over compactness of
their constituents. This severely limits the usefulness of OpenIE extractions
in many downstream tasks. The utility of extractions can be improved if
extractions are compact and share constituents. To this end, we study the
problem of identifying compact extractions with neural-based methods. We
propose CompactIE, an OpenIE system that uses a novel pipelined approach to
produce compact extractions with overlapping constituents. It first detects
constituents of the extractions and then links them to build extractions. We
train our system on compact extractions obtained by processing existing
benchmarks. Our experiments on CaRB and Wire57 datasets indicate that CompactIE
finds 1.5x-2x more compact extractions than previous systems, with high
precision, establishing a new state-of-the-art performance in OpenIE.
