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Transformer-based models have achieved state-of-the-art results in a widerange of natural language processing (NLP) tasks including documentsummarization. Typically these systems are trained by fine-tuning a largepre-trained model to the target task. One issue with these transformer-basedmodels is that they do not scale well in terms of memory and computerequirements as the input length grows. Thus, for long document summarization,it can be challenging to train or fine-tune these models. In this work, weexploit large pre-trained transformer-based models and address long-spandependencies in abstractive summarization using two methods: localself-attention; and explicit content selection. These approaches are comparedon a range of network configurations. Experiments are carried out on standardlong-span summarization tasks, including Spotify Podcast, arXiv, and PubMeddatasets. We demonstrate that by combining these methods, we can achievestate-of-the-art results on all three tasks in the ROUGE scores. Moreover,without a large-scale GPU card, our approach can achieve comparable or betterresults than existing approaches.

Long-Span Summarization via Local Attention and Content Selection