Cognitive Representation Learning of Self-Media Online Article Quality

TL;DR

This paper introduces CoQAN, a joint model for assessing self-media online article quality by integrating layout, writing, and semantic features, tailored to user cognitive styles, and validated on a large real-world dataset.

Contribution

The paper presents a novel multi-modal, cognitive-inspired model for online article quality assessment, incorporating layout, writing, and semantic features with specialized subnetworks.

Findings

Significantly outperforms existing methods in quality assessment accuracy.

Effectively integrates multi-modal features and user reading habits.

Validated on a large-scale real-world dataset.

Abstract

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.