Automatic Addition of SEO Metadata to News Articles Using Qwen-Coder

A previously developed pipeline for enriching news articles with structured data is summarized, and an updated configuration is presented in which GPT-3–OpenAI’s third-generation natural language processing model is replaced with Qwen-Coder. As previously, the updated enrichment pipeline uses a dataset of 400 pages selected from Google News, a free news aggregator provided by Google, remains compatible with the Google Rich Results Test (Google’s tool for validating eligible structured results), and demonstrates that GPT-3-comparable output quality can be achieved on a low-power desktop PC. We describe how this substitution reduces dependence on paid GPT services and report an evaluation comparing the similarity of outputs produced by Qwen-Coder against the GPT-based baseline. The results also show the better performance of the new algorithm compared with the GPT version. The proposed tools lower the barrier to adopting semantic markup practices and thereby broaden their application in digital journalism. Overall, the findings support Qwen-Coder as a cost-effective alternative to large proprietary models for metadata enrichment tasks.