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 SummarizationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Abstractive Text Summarization | 20 | 27.40% |
Text Summarization | 11 | 15.07% |
Document Summarization | 5 | 6.85% |
Decoder | 4 | 5.48% |
Sentence | 3 | 4.11% |
Multi-Document Summarization | 3 | 4.11% |
Text Generation | 3 | 4.11% |
Active Learning | 3 | 4.11% |
Domain Adaptation | 2 | 2.74% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |