  Risk mining technologies seek to find relevant textual extractions that
capture entity-risk relationships. However, when high volume data sets are
processed, a multitude of relevant extractions can be returned, shifting the
focus to how best to present the results. We provide the details of a risk
mining multi-document extractive summarization system that produces high
quality output by modeling shifts in specificity that are characteristic of
well-formed discourses. In particular, we propose a novel selection algorithm
that alternates between extracts based on human curated or expanded autoencoded
key terms, which exhibit greater specificity or generality as it relates to an
entity-risk relationship. Through this extract ordering, and without the need
for more complex discourse-aware NLP, we induce felicitous shifts in
specificity in the alternating summaries that outperform non-alternating
summaries on automatic ROUGE and BLEU scores, and manual understandability and
preferences evaluations - achieving no statistically significant difference
when compared to human authored summaries.
