Fetching the paper…
Reading the bibliography…
It is well known that the standard likelihood training and approximate decoding objectives in neural text generation models lead to less human-like responses for open-ended tasks such as language modeling and story generation.
Sample efficient text summarization using a single pre-trained transformer
Urvashi Khandelwal, Kevin Clark, Dan Jurafsky, and Lukasz Kaiser. 2019 · 1905
Earlier work this paper cites.
XLNet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019 · 1906
Earlier work this paper cites.
RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Leveraging pre-trained checkpoints for sequence generation tasks
Sascha Rothe, Shashi Narayan, and Aliaksei Severyn. 2020 · 1907
Earlier work this paper cites.
Evaluating the factual consistency of abstractive text summarization
Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2019b · 1910
Earlier work this paper cites.
The measurement of observer agreement for categorical data
J. Richard Landis and Gary G. Koch. 1977 · 1977
Earlier work this paper cites.
Cognitive psychology and discourse: Recalling and summarizing stories
Teun A. van Dijk and Walter Kintsch. 1978 · 1978
Earlier work this paper cites.
Macrorules for summarizing texts: The development of expertise
Ann L. Brown and Jeanne D. Day. 1983 · 1983
Earlier work this paper cites.
Automatic summarization , volume 3
Inderjeet Mani. 2001 · 2001
Earlier work this paper cites.
Automatic evaluation of summaries using n-gram co-occurrence statistics
Chin Yew Lin and Eduard Hovy. 2003 · 2003
Earlier work this paper cites.
QURIOUS: Question generation pretraining for text generation
Shashi Narayan, Gonçalo Simoes, Ji Ma, Hannah Craighead, and Ryan T. McDonald. 2020 · 2004
Earlier work this paper cites.
Evaluating content selection in summarization: The Pyramid method
Ani Nenkova and Rebecca Passonneau. 2004 · 2004
Earlier work this paper cites.
Overview of DUC 2005
Hoa Trang Dang. 2005 · 2005
Earlier work this paper cites.
Automatic Text Summarization of Newswire: Lessons Learned from the Document Understanding Conference
Ani Nenkova. 2005 · 2005
Earlier work this paper cites.
Open information extraction from the web
Michele Banko, Michael J. Cafarella, Stephen Soderland, Matt Broadhead, and Oren Etzioni. 2007 · 2007
Earlier work this paper cites.
The New York Times Annotated Corpus
Evan Sandhaus. 2008 · 2008
Earlier work this paper cites.
Automatic summarization
Ani Nenkova and Kathleen McKeown. 2011 · 2011
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O.K. Li. 2016 · 2016
Cited alongside, same era.
Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli. 2016 · 2016
Cited alongside, same era.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016 · 2016
Cited alongside, same era.
Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017 · 2017
Cited alongside, same era.
The limits of automatic summarisation according to rouge
Natalie Schluter. 2017 · 2017
Guiding extractive summarization with question-answering rewards
Kristjan Arumae and Fei Liu. 2019 · 2019
Later among the works it cites.
Transformer-XL: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, and Ruslan Salakhutdinov. 2019 · 2019
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
Handling divergent reference texts when evaluating table-to-text generation
Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, Ming-Wei Chang, Dipanjan Das, and William Cohen. 2019 · 2019
Later among the works it cites.
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Challenges in data-to-document generation
Sam Wiseman, Stuart Shieber, and Alexander Rush. 2017 · 2017
Cited alongside, same era.
Fast abstractive summarization with reinforce-selected sentence rewriting
Yen-Chun Chen and Mohit Bansal. 2018 · 2018
Cited alongside, same era.
Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 2018 · 2018
Cited alongside, same era.
Taku Kudo and John Richardson. 2018 · 2018
Cited alongside, same era.
Generating wikipedia by summarizing long sequences
Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. 2018 · 2018
Cited alongside, same era.
Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
Tobias Falke, Leonardo F. R. Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych. 2019 · 2019
Later among the works it cites.
Assessing the factual accuracy of generated text
Ben Goodrich, Vinay Rao, Peter J. Liu, and Mohammad Saleh. 2019 · 2019
Later among the works it cites.
HighRES: Highlight-based reference-less evaluation of summarization
Hardy, Shashi Narayan, and Andreas Vlachos. 2019 · 2019
Later among the works it cites.
Neural text summarization: A critical evaluation
Wojciech Kryscinski, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher. 2019a · 2019
Later among the works it cites.
Natural questions: A benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Later among the works it cites.
Probing neural network comprehension of natural language arguments
Timothy Niven and Hung-Yu Kao. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Do massively pretrained language models make better storytellers?
Abigail See, Aneesh Pappu, Rohun Saxena, Akhila Yerukola, and Christopher D. Manning. 2019 · 2019
Later among the works it cites.
MASS: Masked sequence to sequence pre-training for language generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2019 · 2019
Later among the works it cites.
Dialogue natural language inference
Sean Welleck, Jason Weston, Arthur Szlam, and Kyunghyun Cho. 2019 · 2019
Later among the works it cites.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Maxwell Forbes, and Yejin Choi. 2020 · 2020
Closest in time.
Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Michael Lewis. 2020 · 2020
Closest in time.
Neural text generation with unlikelihood training
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2020 · 2020
Closest in time.
BERTScore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
Closest in time.