
Citation
```
@misc{https://doi.org/10.48550/arxiv.2110.07166,
  doi = {10.48550/ARXIV.2110.07166},
  url = {https://arxiv.org/abs/2110.07166},
  author = {Choubey, Prafulla Kumar and Fabbri, Alexander R. and Vig, Jesse and Wu, Chien-Sheng and Liu, Wenhao and Rajani, Nazneen Fatema},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization},
  publisher = {arXiv},
  year = {2021},
  copyright = {Creative Commons Attribution 4.0 International}
}
```
{"language": "en", "license": "bsd-3-clause", "tags": ["summarization"], "datasets": ["cnn_dailymail"]}
transformers pytorch bart text2text-generation summarization en dataset:cnn_dailymail arxiv:2110.07166 license:bsd-3-clause autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #arxiv-2110.07166 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us 


Citation

TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #arxiv-2110.07166 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #arxiv-2110.07166 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us 
