Fetching the paper…
Reading the bibliography…
Few-shot abstractive summarization has become a challenging task in natural language generation.
Few-shot learning for opinion summarization
Arthur Bražinskas, Mirella Lapata, and Ivan Titov. 2020 · 2004
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2005
Earlier work this paper cites.
Alexander R Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, and Yashar Mehdad. 2020 · 2010
Earlier work this paper cites.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2010
Earlier work this paper cites.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2020 · 2012
Earlier work this paper cites.
Few-shot text generation with pattern-exploiting training
Timo Schick and Hinrich Schütze. 2020 · 2012
Earlier work this paper cites.
The stanford corenlp natural language processing toolkit
Christopher D Manning, Mihai Surdeanu, John Bauer, Jenny Rose Finkel, Steven Bethard, and David McClosky. 2014 · 2014
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Best-worst scaling more reliable than rating scales: A case study on sentiment intensity annotation
Svetlana Kiritchenko and Saif M Mohammad. 2017 · 2017
Earlier work this paper cites.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
Earlier work this paper cites.
Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Don’t give me the details, just the summary! Topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Cited alongside, same era.
Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
Cited alongside, same era.
Concept pointer network for abstractive summarization
Wenbo Wang, Yang Gao, He-Yan Huang, and Yuxiang Zhou. 2019 · 2019
Cited alongside, same era.
Selfore: Self-supervised relational feature learning for open relation extraction
Xuming Hu, Lijie Wen, Yusong Xu, Chenwei Zhang, and S Yu Philip. 2020 · 2020
Cited alongside, same era.
Cross-lingual abstractive summarization with limited parallel resources
Yu Bai, Yang Gao, and Heyan Huang. 2021 · 2021
Later among the works it cites.
Ppt: Pre-trained prompt tuning for few-shot learning
Yuxian Gu, Xu Han, Zhiyuan Liu, and Minlie Huang. 2021 · 2021
Later among the works it cites.
Gradient imitation reinforcement learning for low resource relation extraction
Xuming Hu, Chenwei Zhang, Yawen Yang, Xiaohe Li, Li Lin, Lijie Wen, and S Yu Philip. 2021 · 2021
Later among the works it cites.
Woojeong Jin, Yu Cheng, Yelong Shen, Weizhu Chen, and Xiang Ren. 2021 · 2021
Later among the works it cites.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020 · 2020
Cited alongside, same era.
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
Multilingual denoising pre-training for neural machine translation
Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Julien Chaumond, Lysandre Debut, Victor Sanh, Clement Delangue, Anthony Moi, Pierric Cistac, Morgan Funtowicz, Joe Davison, Sam Shleifer, et al. 2020 · 2020
Cited alongside, same era.
Ted: A pretrained unsupervised summarization model with theme modeling and denoising
Ziyi Yang, Chenguang Zhu, Robert Gmyr, Michael Zeng, Xuedong Huang, and Eric Darve. 2020 · 2020
Cited alongside, same era.
Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020 · 2020
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Later among the works it cites.
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021 · 2021
Later among the works it cites.
Cutting down on prompts and parameters: Simple few-shot learning with language models
Robert L Logan IV, Ivana Balažević, Eric Wallace, Fabio Petroni, Sameer Singh, and Sebastian Riedel. 2021 · 2021
Later among the works it cites.
True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
Later among the works it cites.
Exploring explainable selection to control abstractive summarization
Haonan Wang, Yang Gao, Yu Bai, Mirella Lapata, and Heyan Huang. 2021 · 2021
Later among the works it cites.
Adaptsum: Towards low-resource domain adaptation for abstractive summarization
Tiezheng Yu, Zihan Liu, and Pascale Fung. 2021 · 2021
Later among the works it cites.