  Identification of new concepts in scientific literature can help power
faceted search, scientific trend analysis, knowledge-base construction, and
more, but current methods are lacking. Manual identification cannot keep up
with the torrent of new publications, while the precision of existing automatic
techniques is too low for many applications. We present an unsupervised concept
extraction method for scientific literature that achieves much higher precision
than previous work. Our approach relies on a simple but novel intuition: each
scientific concept is likely to be introduced or popularized by a single paper
that is disproportionately cited by subsequent papers mentioning the concept.
From a corpus of computer science papers on arXiv, we find that our method
achieves a Precision@1000 of 99%, compared to 86% for prior work, and a
substantially better precision-yield trade-off across the top 15,000
extractions. To stimulate research in this area, we release our code and data
(https://github.com/allenai/ForeCite).
