  Automatic comment generation is a special and challenging task to verify the
model ability on news content comprehension and language generation. Comments
not only convey salient and interesting information in news articles, but also
imply various and different reader characteristics which we treat as the
essential clues for diversity. However, most of the comment generation
approaches only focus on saliency information extraction, while the
reader-aware factors implied by comments are neglected. To address this issue,
we propose a unified reader-aware topic modeling and saliency information
detection framework to enhance the quality of generated comments. For
reader-aware topic modeling, we design a variational generative clustering
algorithm for latent semantic learning and topic mining from reader comments.
For saliency information detection, we introduce Bernoulli distribution
estimating on news content to select saliency information. The obtained topic
representations as well as the selected saliency information are incorporated
into the decoder to generate diversified and informative comments. Experimental
results on three datasets show that our framework outperforms existing baseline
methods in terms of both automatic metrics and human evaluation. The potential
ethical issues are also discussed in detail.
