  As a concise form of user reviews, tips have unique advantages to explain the
search results, assist users' decision making, and further improve user
experience in vertical search scenarios. Existing work on tip generation does
not take query into consideration, which limits the impact of tips in search
scenarios. To address this issue, this paper proposes a query-aware tip
generation framework, integrating query information into encoding and
subsequent decoding processes. Two specific adaptations of Transformer and
Recurrent Neural Network (RNN) are proposed. For Transformer, the query impact
is incorporated into the self-attention computation of both the encoder and the
decoder. As for RNN, the query-aware encoder adopts a selective network to
distill query-relevant information from the review, while the query-aware
decoder integrates the query information into the attention computation during
decoding. The framework consistently outperforms the competing methods on both
public and real-world industrial datasets. Last but not least, online
deployment experiments on Dianping demonstrate the advantage of the proposed
framework for tip generation as well as its online business values.
