  Highly specific datasets of scientific literature are important for both
research and education. However, it is difficult to build such datasets at
scale. A common approach is to build these datasets reductively by applying
topic modeling on an established corpus and selecting specific topics. A more
robust but time-consuming approach is to build the dataset constructively in
which a subject matter expert (SME) handpicks documents. This method does not
scale and is prone to error as the dataset grows. Here we showcase a new tool,
based on machine learning, for constructively generating targeted datasets of
scientific literature. Given a small initial "core" corpus of papers, we build
a citation network of documents. At each step of the citation network, we
generate text embeddings and visualize the embeddings through dimensionality
reduction. Papers are kept in the dataset if they are "similar" to the core or
are otherwise pruned through human-in-the-loop selection. Additional insight
into the papers is gained through sub-topic modeling using SeNMFk. We
demonstrate our new tool for literature review by applying it to two different
fields in machine learning.
