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Summarization systems make numerous "decisions" about summary properties during inference, e.g.
Ctrl: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher. 2019 · 1909
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A new readability yardstick
Rudolph Flesch. 1948 · 1948
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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Fast and accurate prediction of sentence specificity
Junyi Jessy Li and Ani Nenkova. 2015 · 2015
Earlier work this paper cites.
A neural attention model for abstractive sentence summarization
Alexander M Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Earlier work this paper cites.
Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang. 2016 · 2016
Earlier work this paper cites.
Controlling linguistic style aspects in neural language generation
Jessica Ficler and Yoav Goldberg. 2017 · 2017
Earlier work this paper cites.
Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing. 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.
Style transfer from non-parallel text by cross-alignment
Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2017 · 2017
Earlier work this paper cites.
Steering output style and topic in neural response generation
Di Wang, Nebojsa Jojic, Chris Brockett, and Eric Nyberg. 2017 · 2017
Earlier work this paper cites.
Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies
Max Grusky, Mor Naaman, and Yoav Artzi. 2018 · 2018
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.
Diverse beam search for improved description of complex scenes
Ashwin K Vijayakumar, Michael Cogswell, Ramprasaath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. 2018 · 2018
Cited alongside, same era.
Unifying human and statistical evaluation for natural language generation
Tatsunori Hashimoto, Hugh Zhang, and Percy Liang. 2019 · 2019
Cited alongside, same era.
Hierarchically structured reinforcement learning for topically coherent visual story generation
Qiuyuan Huang, Zhe Gan, Asli Celikyilmaz, Dapeng Wu, Jianfeng Wang, and Xiaodong He. 2019 · 2019
Cited alongside, same era.
Controlling the amount of verbatim copying in abstractive summarization
Kaiqiang Song, Bingqing Wang, Zhe Feng, Ren Liu, and Fei Liu. 2020 · 2020
Later among the works it cites.
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
Later among the works it cites.
Understanding neural abstractive summarization models via uncertainty
Jiacheng Xu, Shrey Desai, and Greg Durrett. 2020 · 2020
Later among the works it cites.
Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020 · 2020
Later among the works it cites.
Summeval: Re-evaluating summarization evaluation
Alexander Richard Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2021 · 2021
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Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation
Ashutosh Kumar, Satwik Bhattamishra, Manik Bhandari, and Partha Talukdar. 2019 · 2019
Cited alongside, same era.
Paraphrase diversification using counterfactual debiasing
Sunghyun Park, Seung-won Hwang, Fuxiang Chen, Jaegul Choo, Jung-Woo Ha, Sunghun Kim, and Jinyeong Yim. 2019 · 2019
Cited alongside, same era.
Long and diverse text generation with planning-based hierarchical variational model
Zhihong Shao, Minlie Huang, Jiangtao Wen, Wenfei Xu, and Xiaoyan Zhu. 2019 · 2019
Cited alongside, same era.
Flight of the pegasus? comparing transformers on few-shot and zero-shot multi-document abstractive summarization
Travis Goodwin, Max Savery, and Dina Demner-Fushman. 2020 · 2020
Cited alongside, same era.
Neural syntactic preordering for controlled paraphrase generation
Tanya Goyal and Greg Durrett. 2020 · 2020
Cited alongside, same era.
Reformulating unsupervised style transfer as paraphrase generation
Kalpesh Krishna, John Wieting, and Mohit Iyyer. 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.
Annotating and modeling fine-grained factuality in summarization
Tanya Goyal and Greg Durrett. 2021 · 2021
Closest in time.
The perils of using mechanical turk to evaluate open-ended text generation
Marzena Karpinska, Nader Akoury, and Mohit Iyyer. 2021 · 2021
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GeDi: Generative discriminator guided sequence generation
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, and Nazneen Fatema Rajani. 2021 · 2021
Closest in time.
A new approach to overgenerating and scoring abstractive summaries
Kaiqiang Song, Bingqing Wang, Zhe Feng, and Fei Liu. 2021 · 2021
Closest in time.
Training dynamics for text summarization models
Tanya Goyal, Jiacheng Xu, Junyi Jessy Li, and Greg Durrett. 2022 · 2022
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Ctrlsum: Towards generic controllable text summarization
Junxian He, Wojciech Kryściński, Bryan McCann, Nazneen Rajani, and Caiming Xiong. 2022 · 2022
Closest in time.