Ground Truth Summary

Ground truth summaries are human-generated summaries used as benchmarks to evaluate automated text and video summarization algorithms. Current research focuses on creating high-quality ground truth datasets for diverse domains, including 360° videos, disaster tweets, and news articles in multiple languages, often employing hybrid annotation approaches to improve efficiency and consistency. These datasets are crucial for training and evaluating supervised machine learning models, particularly deep learning architectures like seq2seq models and variations thereof, leading to improved performance in automated summarization across various media types. The availability of robust ground truth summaries significantly advances the field by providing objective measures for algorithm comparison and driving the development of more effective summarization technologies.