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Article summary:

1. This paper proposes a graph convolution network model (PersonaTKG) that integrates personas, dialogue utterances, and external text knowledge in a unified graph.

2. The proposed model is tested on a multi-party personalized dialogue dataset and outperforms the baselines by large margins.

3. The model effectively improves persona consistency in the generated responses.

Article analysis:

The article is generally reliable and trustworthy as it provides evidence for its claims through extensive experiments and results from the proposed model. The authors have also provided references to other related works which adds to the credibility of their research. However, there are some points of consideration that could be explored further such as potential risks associated with using this model or any possible counterarguments that could be made against it. Additionally, the article does not provide an equal representation of both sides of the argument which could lead to partiality in its reporting. Furthermore, there is no mention of any promotional content or unsupported claims which makes it more reliable and trustworthy overall.