Contrastive Learning with High-Quality and Low-Quality Augmented Data for Query-Focused Summarization

Unlike general text summarization, Query-focused summarization (QFS) is severely limited by insufficient datasets, forcing previous research to transform datasets from other tasks into QFS format for data augmentation. However, this approach has resulted in two problems: the task and traintest gaps. To alleviate these gaps, we propose QFS-CL, a novel in-place data augmentation framework equipped with contrastive learning. Firstly, we design diverse prompts for ChatGPT to paraphrase the original QFS data into high-quality/low-quality document-summary pairs, filling the task gap. Then, instead of directly incorporating the augmented data into the training set, we train the QFS baseline model in a contrastive learning scheme. Specifically, our approach encourages the model to imitate high-quality pairs and distinguish itself from low-quality pairs, enabling the model to learn how to acquire reliable information and avoid extracting invalid information. Our method achieves state-of-the-art performance on Debatepedia and DUC datasets in ROUGE scores, GPT-4, and human evaluations.