  We argue that disentangling content selection from the budget used to cover
salient content improves the performance and applicability of abstractive
summarizers. Our method, FactorSum, does this disentanglement by factorizing
summarization into two steps through an energy function: (1) generation of
abstractive summary views; (2) combination of these views into a final summary,
following a budget and content guidance. This guidance may come from different
sources, including from an advisor model such as BART or BigBird, or in oracle
mode -- from the reference. This factorization achieves significantly higher
ROUGE scores on multiple benchmarks for long document summarization, namely
PubMed, arXiv, and GovReport. Most notably, our model is effective for domain
adaptation. When trained only on PubMed samples, it achieves a 46.29 ROUGE-1
score on arXiv, which indicates a strong performance due to more flexible
budget adaptation and content selection less dependent on domain-specific
textual structure.
