  We present a method for generating comparative summaries that highlights
similarities and contradictions in input documents. The key challenge in
creating such summaries is the lack of large parallel training data required
for training typical summarization systems. To this end, we introduce a hybrid
generation approach inspired by traditional concept-to-text systems. To enable
accurate comparison between different sources, the model first learns to
extract pertinent relations from input documents. The content planning
component uses deterministic operators to aggregate these relations after
identifying a subset for inclusion into a summary. The surface realization
component lexicalizes this information using a text-infilling language model.
By separately modeling content selection and realization, we can effectively
train them with limited annotations. We implemented and tested the model in the
domain of nutrition and health -- rife with inconsistencies. Compared to
conventional methods, our framework leads to more faithful, relevant and
aggregation-sensitive summarization -- while being equally fluent.
