  It is time-consuming to find the best product among many similar
alternatives. Comparative sentences can help to contrast one item from others
in a way that highlights important features of an item that stand out. Given
reviews of one or multiple items and relevant item features, we generate
comparative review sentences to aid users to find the best fit. Specifically,
our model consists of three successive components in a transformer: (i) an item
encoding module to encode an item for comparison, (ii) a comparison generation
module that generates comparative sentences in an autoregressive manner, (iii)
a novel decoding method for user personalization. We show that our pipeline
generates fluent and diverse comparative sentences. We run experiments on the
relevance and fidelity of our generated sentences in a human evaluation study
and find that our algorithm creates comparative review sentences that are
relevant and truthful.
