Towards Table-to-Text Generation for Summarising Machine Learning Models Performance

This paper presents a study on fine-tuning the pre-trained language models such as T5 to generate analytical textual summaries, describing the classification performance of machine learning models. The generation is based on the evaluation metrics achieved on a given classification problem. Evaluation of the generated metrics' narrations, indicates that exploring pre-trained models for data-to-text generation leads to better generalisation performance and can produce high-quality analytical summaries.