arXiv:1903.10318
공식 코드 · Papers with Code
Fine-tune BERT for Extractive Summarization
원문 초록
BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks. In this paper, we describe BERTSUM, a simple variant of BERT, for extractive summarization. Our system is the state of the art on the CNN/Dailymail dataset, outperforming the previous best-performed system by 1.65 on ROUGE-L. The codes to reproduce our results are available at https://github.com/nlpyang/BertSum
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Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers
Heterogeneous Graph Neural Networks for Extractive Document Summarization
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
Combining Word Embeddings and N-grams for Unsupervised Document Summarization
At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization
Neural Latent Extractive Document Summarization
토론
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