ML6 team has shared two large machine-translated Dutch summarization datasets with the Hugging Face community, providing valuable resources for Dutch NLP tasks. The datasets are translations of English news articles from CNN, Dailymail, and BBC (XSum). We used the Opus MT model for the translations, which required significant computational resources. These datasets are beneficial for training machine learning models to automatically summarize Dutch news articles.
In the blogpost we discuss the concept of transfer learning and how it can be applied to Dutch summarization tasks. By leveraging pre-trained models and sequential adaptation techniques, we improve the model's performance on the axes of summarization, Dutch language understanding, and news domain knowledge. We provide example summaries and evaluate the results, ultimately demonstrating the usefulness of the machine-translated datasets for enhancing Dutch news summarization models. Additionally, we open-sourced the datasets and the final fine-tuned Dutch news summarization model for others to use and explore.
The blogpost can be found on our Medium channel by clicking this link.