  Relevance in summarization is typically defined based on textual information
alone, without incorporating insights about a particular decision. As a result,
to support risk analysis of pancreatic cancer, summaries of medical notes may
include irrelevant information such as a knee injury. We propose a novel
problem, decision-focused summarization, where the goal is to summarize
relevant information for a decision. We leverage a predictive model that makes
the decision based on the full text to provide valuable insights on how a
decision can be inferred from text. To build a summary, we then select
representative sentences that lead to similar model decisions as using the full
text while accounting for textual non-redundancy. To evaluate our method
(DecSum), we build a testbed where the task is to summarize the first ten
reviews of a restaurant in support of predicting its future rating on Yelp.
DecSum substantially outperforms text-only summarization methods and
model-based explanation methods in decision faithfulness and
representativeness. We further demonstrate that DecSum is the only method that
enables humans to outperform random chance in predicting which restaurant will
be better rated in the future.
