Lead bias is a common phenomenon in news summarization, where early parts of an article often contain the most salient information. While many algorithms exploit this fact in summary generation, it has a detrimental effect on teaching the model to discriminate and extract important information. We propose that the lead bias can be leveraged in a simple and effective way in our favor to pre-train abstractive news summarization models on large-scale unlabeled corpus: predicting the leading sentences using the rest of an article. We collect a massive news corpus and conduct careful data cleaning and filtering. We then apply the proposed self-supervised pre-training to existing generation models BART and T5. Via extensive experiments on six benchmark datasets, we show that this approach can dramatically improve the quality of summary and achieve state-of-the-art results for zero-shot news summarization without any fine-tuning. For example, in the DUC-2003 dataset, the ROUGE-1 of BART increases 13.7% after the lead-bias pre-training.<\/p>\n","protected":false},"excerpt":{"rendered":"
Lead bias is a common phenomenon in news summarization, where early parts of an article often contain the most salient information. While many algorithms exploit this fact in summary generation, it has a detrimental effect on teaching the model to discriminate and extract important information. We propose that the lead bias can be leveraged in 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