Semi-supervised Text Summarization

Semi-supervised text summarization trains summarization models by leveraging large amounts of unlabeled text alongside a small set of human-written reference summaries. By using techniques such as language-model pretraining, pseudo-labeling, and self-training, these methods substantially reduce the annotation burden while maintaining competitive ROUGE scores on benchmark datasets.