Bidirectional Encoder Representations from Transformers (BERT) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper, we showcase how BERT can be usefully applied in text summarization and propose a general framework for both extractive and abstractive models... (read more)
PDFSOTA for Extractive Document Summarization on CNN / Daily Mail (using extra training data)
TASK | DATASET | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK | USES EXTRA TRAINING DATA |
COMPARE |
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Extractive Document Summarization | CNN / Daily Mail | BertSumExt | ROUGE-2 | 20.34 | # 1 |
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Extractive Document Summarization | CNN / Daily Mail | BertSumExt | ROUGE-1 | 43.85 | # 2 |
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Extractive Document Summarization | CNN / Daily Mail | BertSumExt | ROUGE-L | 39.90 | # 1 |
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Document Summarization | CNN / Daily Mail | BertSumExt | ROUGE-1 | 43.85 | # 1 |
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Document Summarization | CNN / Daily Mail | BertSumExt | ROUGE-2 | 20.34 | # 3 |
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Document Summarization | CNN / Daily Mail | BertSumExt | ROUGE-L | 39.90 | # 3 |
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Abstractive Text Summarization | CNN / Daily Mail | BertSumExtAbs | ROUGE-1 | 42.13 | # 2 |
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Abstractive Text Summarization | CNN / Daily Mail | BertSumExtAbs | ROUGE-2 | 19.60 | # 2 |
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Abstractive Text Summarization | CNN / Daily Mail | BertSumExtAbs | ROUGE-L | 39.18 | # 2 |
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