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Sequence-to-sequence neural networks have recently achieved great success in abstractive summarization, especially through fine-tuning large pre-trained language models on the downstream dataset.
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Automatic evaluation of summaries using n-gram co-occurrence statistics
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Discriminative models for information retrieval
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Better fine-tuning by reducing representational collapse
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Abstractive text summarization using sequence-to-sequence RNNs and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Çağlar Gu̇lçehre, and Bing Xiang. 2016 · 2016
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Diverse beam search: Decoding diverse solutions from neural sequence models
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017 · 2017
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A discourse-aware attention model for abstractive summarization of long documents
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018 · 2018
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
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Transformers: State-of-the-art natural language processing
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Energy-based reranking: Improving neural machine translation using energy-based models
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Abstractive summarization of Reddit posts with multi-level memory networks
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GSum: A general framework for guided neural abstractive summarization
Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi, Zhengbao Jiang, and Graham Neubig. 2021 · 2021
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Summeval: Re-evaluating summarization evaluation
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Evaluating the efficacy of summarization evaluation across languages
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SimCLS: A simple framework for contrastive learning of abstractive summarization
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ProphetNet-X: Large-scale pre-training models for English, Chinese, multi-lingual, dialog, and code generation
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