Prompt-based Pseudo-labeling Strategy for Sample-Efficient Semi-Supervised Extractive Summarization

Semi-supervised learning (SSL) is a widely used technique in scenarios where labeled data is scarce and unlabeled data is abundant. While SSL is popular for image and text classification, it is relatively underexplored for the task of extractive text summarization. Standard SSL methods follow a teacher-student paradigm to first train a classification model and then use the classifier's confidence values to select pseudo-labels for the subsequent training cycle; however, such classifiers are not suitable to measure the accuracy of pseudo-labels as they lack specific tuning for evaluation, which leads to confidence values that fail to capture the semantics and correctness of the generated summary. To address this problem, we propose a prompt-based pseudo-labeling strategy with LLMs that picks unlabeled examples with more accurate pseudo-labels than using just the classifier's probability outputs. Our approach also includes a relabeling mechanism that improves the quality of pseudo-labels. We evaluate our method on three text summarization datasets: TweetSumm, WikiHow, and ArXiv/PubMed. We empirically show that a prompting-based LLM that scores and generates pseudo-labels outperforms existing SSL methods on ROUGE-1, ROUGE-2, and ROUGE-L scores on all the datasets. Furthermore, our method achieves competitive G-Eval scores (evaluation with GPT-4) as a fully supervised method that uses 100% of the labeled data with only 16.67% of the labeled data.