  The automatic quality assessment of self-media online articles is an urgent
and new issue, which is of great value to the online recommendation and search.
Different from traditional and well-formed articles, self-media online articles
are mainly created by users, which have the appearance characteristics of
different text levels and multi-modal hybrid editing, along with the potential
characteristics of diverse content, different styles, large semantic spans and
good interactive experience requirements. To solve these challenges, we
establish a joint model CoQAN in combination with the layout organization,
writing characteristics and text semantics, designing different representation
learning subnetworks, especially for the feature learning process and
interactive reading habits on mobile terminals. It is more consistent with the
cognitive style of expressing an expert's evaluation of articles. We have also
constructed a large scale real-world assessment dataset. Extensive experimental
results show that the proposed framework significantly outperforms
state-of-the-art methods, and effectively learns and integrates different
factors of the online article quality assessment.
