PEGASUS proposes a transformer-based model for abstractive summarization. It uses a special self-supervised pre-training objective called gap-sentences generation (GSG) that's designed to perform well on summarization-related downstream tasks. As reported in the paper, "both GSG and MLM are applied simultaneously to this example as pre-training objectives. Originally there are three sentences. One sentence is masked with [MASK1] and used as target generation text (GSG). The other two sentences remain in the input, but some tokens are randomly masked by [MASK2]."

Source: PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization


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🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign