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We propose encoder-centric stepwise models for extractive summarization usingstructured transformers -- HiBERT and Extended Transformers. We enable stepwisesummarization by injecting the previously generated summary into the structuredtransformer as an auxiliary sub-structure. Our models are not only efficient inmodeling the structure of long inputs, but they also do not rely ontask-specific redundancy-aware modeling, making them a general purposeextractive content planner for different tasks. When evaluated on CNN/DailyMailextractive summarization, stepwise models achieve state-of-the-art performancein terms of Rouge without any redundancy aware modeling or sentence filtering.This also holds true for Rotowire table-to-text generation, where our modelssurpass previously reported metrics for content selection, planning andordering, highlighting the strength of stepwise modeling. Amongst the twostructured transformers we test, stepwise Extended Transformers provides thebest performance across both datasets and sets a new standard for thesechallenges.

Stepwise Extractive Summarization and Planning with Structured Transformers