Set diffusion factorizes likelihood over arbitrary token sets and uses a set-causal diffusion architecture to support KV caching and any-order decoding, yielding improved speed-quality tradeoffs versus prior diffusion LMs.
Text summariza- tion with pretrained encoders
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
BART introduces a denoising pretraining method for seq2seq models that matches RoBERTa on GLUE and SQuAD while setting new state-of-the-art results on abstractive summarization, dialogue, and QA with up to 6 ROUGE gains.
A normalizing-flow neural topic model plus control mechanism are added to Transformer summarizers to supply and regulate global semantics, with reported gains over prior models on five benchmarks.
citing papers explorer
-
Set Diffusion: Interpolating Token Orderings Between Autoregression and Diffusion for Fast and Flexible Decoding
Set diffusion factorizes likelihood over arbitrary token sets and uses a set-causal diffusion architecture to support KV caching and any-order decoding, yielding improved speed-quality tradeoffs versus prior diffusion LMs.
-
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
BART introduces a denoising pretraining method for seq2seq models that matches RoBERTa on GLUE and SQuAD while setting new state-of-the-art results on abstractive summarization, dialogue, and QA with up to 6 ROUGE gains.
-
Enriching and Controlling Global Semantics for Text Summarization
A normalizing-flow neural topic model plus control mechanism are added to Transformer summarizers to supply and regulate global semantics, with reported gains over prior models on five benchmarks.