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Models/LEAD-3

LEAD-3

Reported on 6 benchmarks across 2 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Knowledge Base3 results

  • Text SummarizationonCNN / Daily Mail
    ROUGE-1· 2016-02-19
    40.42
    best: 48.18 (Scrambled code + broken (alter))
    SOTA
    Abstractive Text Summarization Using Sequence-to-Sequence RNNs and BeyondarXiv:1602.06023
  • Text SummarizationonCNN / Daily Mail
    ROUGE-2· 2016-02-19
    17.62
    best: 24.02 (Pegasus)
    SOTA
    Abstractive Text Summarization Using Sequence-to-Sequence RNNs and BeyondarXiv:1602.06023
  • Text SummarizationonCNN / Daily Mail
    ROUGE-L· 2016-02-19
    36.67
    best: 45.35 (Scrambled code + broken (alter))
    SOTA
    Abstractive Text Summarization Using Sequence-to-Sequence RNNs and BeyondarXiv:1602.06023

Natural Language Processing3 results

  • Abstractive Text SummarizationonCNN / Daily Mail
    ROUGE-1· 2016-02-19
    40.42
    best: 48.18 (Scrambled code + broken (alter))
    SOTA
    Abstractive Text Summarization Using Sequence-to-Sequence RNNs and BeyondarXiv:1602.06023
  • Abstractive Text SummarizationonCNN / Daily Mail
    ROUGE-2· 2016-02-19
    17.62
    best: 24.02 (Pegasus)
    SOTA
    Abstractive Text Summarization Using Sequence-to-Sequence RNNs and BeyondarXiv:1602.06023
  • Abstractive Text SummarizationonCNN / Daily Mail
    ROUGE-L· 2016-02-19
    36.67
    best: 45.35 (Scrambled code + broken (alter))
    SOTA
    Abstractive Text Summarization Using Sequence-to-Sequence RNNs and BeyondarXiv:1602.06023