  We propose a general framework for topic-specific summarization of large text
corpora, and illustrate how it can be used for analysis in two quite different
contexts: an OSHA database of fatality and catastrophe reports (to facilitate
surveillance for patterns in circumstances leading to injury or death) and
legal decisions on workers' compensation claims (to explore relevant case law).
Our summarization framework, built on sparse classification methods, is a
compromise between simple word frequency based methods currently in wide use,
and more heavyweight, model-intensive methods such as Latent Dirichlet
Allocation (LDA). For a particular topic of interest (e.g., mental health
disability, or chemical reactions), we regress a labeling of documents onto the
high-dimensional counts of all the other words and phrases in the documents.
The resulting small set of phrases found as predictive are then harvested as
the summary. Using a branch-and-bound approach, this method can be extended to
allow for phrases of arbitrary length, which allows for potentially rich
summarization. We discuss how focus on the purpose of the summaries can inform
choices of regularization parameters and model constraints. We evaluate this
tool by comparing computational time and summary statistics of the resulting
word lists to three other methods in the literature. We also present a new R
package, textreg. Overall, we argue that sparse methods have much to offer text
analysis, and is a branch of research that should be considered further in this
context.
