Shashi Narayan
Natural Language Processing · 2017–2022
A researcher in the Science corpus: 8 papers; trusts 372 researchers and is trusted by 270 researchers here, as inferred from the citation graph.
Subfields
- Natural Language Processing — 347 citations into it; #382 of 5,688 by trust here, #412 by citations
- Artificial Intelligence — 342 citations into it; #1,144 of 35,345 by trust here, #1,723 by citations
- Evaluation & Benchmarks — 144 citations into it; #1,128 of 3,147 by trust here, #623 by citations
- Computer Vision and Pattern Recognition — 83 citations into it
- Large Language Models — 54 citations into it
- Model Architectures — 25 citations into it
- Information Systems — 22 citations into it
- Generative Models — 11 citations into it
Whom Shashi Narayan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Ilya Sutskever
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Tomas Mikolov
- Alec Radford
- Kaiming He
- Oriol Vinyals
- Yann LeCun
Who trusts Shashi Narayan overall
- Alexander R. Fabbri
- Sebastian Gehrmann
- Thomas Scialom
- Yang Liu
- Yixin Liu
- Mohit Bansal
- Richard Yuanzhe Pang
- Alex Wang
- Yi Tay
- Pengfei Liu
Papers from Shashi Narayan's perspective
Ordered by how strongly the researchers Shashi Narayan trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (2018) — 2,107 citations
- On Faithfulness and Factuality in Abstractive Summarization (2020) — 1,585 citations
- The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (2021) — 329 citations
- The WebNLG Challenge: Generating Text from RDF Data (2017) — 452 citations
- Planning with Learned Entity Prompts for Abstractive Summarization (2021) — 134 citations
- SMART: Sentences as Basic Units for Text Evaluation (2022) — 27 citations
- QURIOUS: Question Generation Pretraining for Text Generation (2020) — 16 citations
- Focus Attention: Promoting Faithfulness and Diversity in Summarization (2021) — 53 citations