Abstract
Automatic article commenting is helpful in encouraging user engagement and interaction on
online news platforms. However, the news
documents are usually too long for traditional
encoder-decoder based models, which often
results in general and irrelevant comments. In
this paper, we propose to generate comments
with a graph-to-sequence model that models
the input news as a topic interaction graph.
By organizing the article into graph structure,
our model can better understand the internal
structure of the article and the connection between topics, which makes it better able to
understand the story. We collect and release
a large scale news-comment corpus from a
popular Chinese online news platform Tencent
Kuaibao.1 Extensive experiment results show
that our model can generate much more coherent and informative comments compared with
several strong baseline models