Narayan, Shashi
University of Copenhagen
11 papers on CoChat
Most cited papers
- Verified
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
Narayan, S., Cohen, S. B., & Lapata, M. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Topic ModelingAdvanced Text Analysis Techniques
Cited by 1,207
- Verified
On Faithfulness and Factuality in Abstractive Summarization
Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020) · arXiv
Topic ModelingAdvanced Text Analysis Techniques
Cited by 1,185
- Verified
On Faithfulness and Factuality in Abstractive Summarization
Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Topic ModelingAdvanced Text Analysis Techniques
Cited by 1,185
- Verified
Gemini: A Family of Highly Capable Multimodal Models
Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv
Multimodal Machine Learning ApplicationsTopic Modeling
Cited by 990
- Verified
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Aremu, A., Bosselut, A., Chandu, K. R., Clinciu, M.-A., Das, D., Dhole, K., et al. (2021) · Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)
Natural Language Processing TechniquesTopic Modeling
Cited by 159
- Verified
Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation
Tian, R., Narayan, S., Sellam, T., & Parikh, A. P. (2019) · arXiv
Topic ModelingNatural Language Processing Techniques
Cited by 60
- Verified
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Anuoluwapo, A., Bosselut, A., Chandu, K. R., Clinciu, M., Das, D., Dhole, K. D., et al. (2021) · arXiv
Natural Language Processing TechniquesTopic Modeling
Cited by 49
- Verified
Calibrating Sequence likelihood Improves Conditional Language Generation
Zhao, Y., Khalman, M., Joshi, R., Narayan, S., Saleh, M., & Liu, P. J. (2022) · arXiv
Topic ModelingNatural Language Processing Techniques
Cited by 42
- Verified
Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
Narayan, S., Cohen, S. B., & Lapata, M. (2018) · arXiv
Topic ModelingAdvanced Text Analysis Techniques
Cited by 39
- Verified
Conditional Generation with a Question-Answering Blueprint
Narayan, S., Maynez, J., Amplayo, R. K., Ganchev, K., Louis, A., Huot, F., Sandholm, A., Das, D., & Lapata, M. (2022) · arXiv
Topic ModelingNatural Language Processing Techniques
Cited by 24
- Verified
Learning to Plan and Generate Text with Citations
Fierro, C., Amplayo, R. K., Huot, F., De Cao, N., Maynez, J., Narayan, S., & Lapata, M. (2024) · arXiv
Topic ModelingNatural Language Processing Techniques
Cited by 12
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