  Representing documents into high dimensional embedding space while preserving
the structural similarity between document sources has been an ultimate goal
for many works on text representation learning. Current embedding models,
however, mainly rely on the availability of label supervision to increase the
expressiveness of the resulting embeddings. In contrast, unsupervised
embeddings are cheap, but they often cannot capture implicit structure in
target corpus, particularly for samples that come from different distribution
with the pretraining source.
  Our study aims to loosen up the dependency on label supervision by learning
document embeddings via Sequence-to-Sequence (Seq2Seq) text generator.
Specifically, we reformulate keyphrase generation task into multi-label keyword
generation in community-based Question Answering (cQA). Our empirical results
show that KeyGen2Vec in general is superior than multi-label keyword classifier
by up to 14.7% based on Purity, Normalized Mutual Information (NMI), and
F1-Score metrics. Interestingly, although in general the absolute advantage of
learning embeddings through label supervision is highly positive across
evaluation datasets, KeyGen2Vec is shown to be competitive with classifier that
exploits topic label supervision in Yahoo! cQA with larger number of latent
topic labels.
