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We introduce a simple but flexible mechanism to learn an intermediate plan to ground the generation of abstractive summaries.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019 · 1901
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Sample efficient text summarization using a single pre-trained transformer
Urvashi Khandelwal, Kevin Clark, Dan Jurafsky, and Lukasz Kaiser. 2019 · 1905
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Evaluating the factual consistency of abstractive text summarization
Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 1910
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
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The automatic creation of literature abstracts
H. P. Luhn. 1958 · 1958
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Cohesion in English
M. A. K. Halliday and Ruqaiya Hasan. 1976 · 1976
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Rhetorical structure theory: Toward a functional theory of text organization
William C Mann and Sandra A Thompson. 1988 · 1988
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What might be in a summary?
Karen Sparck Jones. 1993 · 1993
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Generating summaries of multiple news articles
Kathleen McKeown and Dragomir R. Radev. 1995 · 1995
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Using lexical chains for text summarization
Regina Barzilay and Michael Elhadad. 1997 · 1997
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Building applied natural language generation systems
Ehud Reiter and Robert Dale. 1997 · 1997
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Using coreference chains for text summarization
Saliha Azzam, Kevin Humphreys, and Robert Gaizauskas. 1999 · 1999
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Automatic evaluation of summaries using n-gram co-occurrence statistics
Chin Yew Lin and Eduard Hovy. 2003 · 2003
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Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
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TextRank: Bringing order into text
Rada Mihalcea and Paul Tarau. 2004 · 2004
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QURIOUS: question generation pretraining for text generation
Shashi Narayan, Gonçalo Simões, Ji Ma, Hannah Craighead, and Ryan T. McDonald. 2020 · 2004
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
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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Globally coherent text generation with neural checklist models
Chloé Kiddon, Luke Zettlemoyer, and Yejin Choi. 2016 · 2016
Cited alongside, same era.
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
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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
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SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019 · 2019
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BillSum: A corpus for automatic summarization of US legislation
Anastassia Kornilova and Vladimir Eidelman. 2019 · 2019
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Data-to-text generation with content selection and planning
Ratish Puduppully, Li Dong, and Mirella Lapata. 2019 · 2019
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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.
Controllable abstractive summarization
Angela Fan, David Grangier, and Michael Auli. 2018 · 2018
Cited alongside, same era.
Event representations for automated story generation with deep neural nets
Lara J. Martin, Prithviraj Ammanabrolu, Xinyu Wang, William Hancock, Shruti Singh, Brent Harrison, and Mark O. Riedl. 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.
Multi-reward reinforced summarization with saliency and entailment
Ramakanth Pasunuru and Mohit Bansal. 2018 · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Cited alongside, same era.
MASS: Masked sequence to sequence pre-training for language generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2019 · 2019
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Plan-and-write: Towards better automatic storytelling
Lili Yao, Nanyun Peng, Ralph M. Weischedel, Kevin Knight, Dongyan Zhao, and Rui Yan. 2019 · 2019
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2019 · 2019
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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. 2020 · 2020
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FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
Esin Durmus, He He, and Mona Diab. 2020 · 2020
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Go figure! a meta evaluation of factuality in summarization
Saadia Gabriel, Asli Celikyilmaz, Rahul Jha, Yejin Choi, and Jianfeng Gao. 2020 · 2020
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Realm: Retrieval-augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2020
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Ctrlsum: Towards generic controllable text summarization
Junxian He, Wojciech Kryściński, Bryan McCann, Nazneen Rajani, and Caiming Xiong. 2020 · 2020
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PAIR: Planning and iterative refinement in pre-trained transformers for long text generation
Xinyu Hua and Lu Wang. 2020 · 2020
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Evaluating the factual consistency of abstractive text summarization
Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2020 · 2020
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Constrained abstractive summarization: Preserving factual consistency with constrained generation
Yuning Mao, Xiang Ren, Heng Ji, and Jiawei Han. 2020 · 2020
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ProphetNet: Predicting future n-gram for sequence-to-SequencePre-training
Weizhen Qi, Yu Yan, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, and Ming Zhou. 2020 · 2020
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