Text Summarization On Reddit Tifu
Metrics
ROUGE-1
ROUGE-2
ROUGE-L
Results
Performance results of various models on this benchmark
Model Name | ROUGE-1 | ROUGE-2 | ROUGE-L | Paper Title | Repository |
|---|---|---|---|---|---|
| MatchSum | 25.09 | 6.17 | 20.13 | Extractive Summarization as Text Matching | |
| MUPPET BART Large | 30.3 | 11.25 | 24.92 | Muppet: Massive Multi-task Representations with Pre-Finetuning | |
| PEGASUS + SummaReranker | 29.83 | 9.5 | 23.47 | SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization | |
| PEGASUS 2B + SLiC | 32.03 | 11.13 | 25.51 | Calibrating Sequence likelihood Improves Conditional Language Generation | - |
| BART+R3F | 30.31 | 10.98 | 24.74 | Better Fine-Tuning by Reducing Representational Collapse |
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