Neural attention models have achieved significant improvements on many
natural language processing tasks. However, the quadratic memory complexity of
the self-attention module with respect to the input length hinders their
applications in long text summarization. Instead of designing more efficient
attention modules, we approach this problem by investigating if models with a
restricted context can have competitive performance compared with the
memory-efficient attention models that maintain a global context by treating
the input as a single sequence. Our model is applied to individual pages which
contain parts of inputs grouped by the principle of locality during both
encoding and decoding. We empirically investigated three kinds of locality in
text summarization at different levels of granularity, ranging from sentences
to documents. Our experimental results show that our model has a better
performance compared with strong baselines with efficient attention modules,
and our analysis provides further insights into our locality-aware modeling
strategy.

本文研究了在长文本摘要场景中采用限定范围的模型是否能够提供比具有全局范围的模型更高的性能，并探讨了在不同层次（从句子到文档）的三种文本局部性，实验结果表明，采用局部化建模策略的模型具有更好的性能。

抽象文本摘要中的地域性优化

Leveraging Locality in Abstractive Text Summarization

Large Transformer models yield impressive results on many tasks, but are
expensive to train, or even fine-tune, and so slow at decoding that their use
and study becomes out of reach. We address this problem by leveraging sparsity.
We study sparse variants for all layers in the Transformer and propose Scaling
Transformers, a family of next generation Transformer models that use sparse
layers to scale efficiently and perform unbatched decoding much faster than the
standard Transformer as we scale up the model size. Surprisingly, the sparse
layers are enough to obtain the same perplexity as the standard Transformer
with the same number of parameters. We also integrate with prior sparsity
approaches to attention and enable fast inference on long sequences even with
limited memory. This results in performance competitive to the state-of-the-art
on long text summarization.

本研究提出了一种用于构建下一代 Transformer 模型的方法，即利用稀疏层进行有效缩放和高效执行非批量解码。结果表明，这种模型在拥有相同参数数量的情况下，可以获得与标准 Transformer 相同的效果，并且在长文本摘要方面表现优异。