We propose encoder-centric stepwise models for extractive summarization using
structured transformers -- HiBERT and Extended Transformers. We enable stepwise
summarization by injecting the previously generated summary into the structured
transformer as an auxiliary sub-structure. Our models are not only efficient in
modeling the structure of long inputs, but they also do not rely on
task-specific redundancy-aware modeling, making them a general purpose
extractive content planner for different tasks. When evaluated on CNN/DailyMail
extractive summarization, stepwise models achieve state-of-the-art performance
in terms of Rouge without any redundancy aware modeling or sentence filtering.
This also holds true for Rotowire table-to-text generation, where our models
surpass previously reported metrics for content selection, planning and
ordering, highlighting the strength of stepwise modeling. Amongst the two
structured transformers we test, stepwise Extended Transformers provides the
best performance across both datasets and sets a new standard for these
challenges.

采用结构化 transformers 和 encoder-centric 模型进行抽取式摘要，通过将前面生成的摘要注入到 transformer 中实现了分步式摘要，同时不依靠任务特定的冗余建模，成为一个通用的抽取式内容规划器。在 CNN / DailyMail 和 Rotowire table-to-text 生成上，我们的模型表现出最先进的性能，并且 extended transformers 模型在这两个数据集上提供了最佳的性能表现。