  A huge volume of user-generated content is daily produced on social media. To
facilitate automatic language understanding, we study keyphrase prediction,
distilling salient information from massive posts. While most existing methods
extract words from source posts to form keyphrases, we propose a
sequence-to-sequence (seq2seq) based neural keyphrase generation framework,
enabling absent keyphrases to be created. Moreover, our model, being
topic-aware, allows joint modeling of corpus-level latent topic
representations, which helps alleviate the data sparsity that widely exhibited
in social media language. Experiments on three datasets collected from English
and Chinese social media platforms show that our model significantly
outperforms both extraction and generation models that do not exploit latent
topics. Further discussions show that our model learns meaningful topics, which
interprets its superiority in social media keyphrase generation.
