Corpus ID: 219708634

DynE: Dynamic Ensemble Decoding for Multi-Document Summarization

@article{Hokamp2020DynEDE,
  title={DynE: Dynamic Ensemble Decoding for Multi-Document Summarization},
  author={Chris Hokamp and Demian Gholipour Ghalandari and N. Pham and John Glover},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.08748}
}
Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing. However, some applications, such as multi-document summarization, multi-modal machine translation, and the automatic post-editing of machine translation, require mapping a set of multiple distinct inputs into a single output sequence. Recent work has introduced bespoke architectures for these multi-input settings, and developed models which can handle increasingly longer inputs; however, the… Expand
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