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Query focused summarization (QFS) models aim to generate summaries from source documents that can answer the given query.
End-to-end open-domain question answering with bertserini
Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019a · 1902
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Mass: Masked sequence to sequence pre-training for language generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2019 · 1905
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019b · 1906
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 1908
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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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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 · 1910
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Dongling Xiao, Han Zhang, Yukun Li, Yu Sun, Hao Tian, Hua Wu, and Haifeng Wang. 2020 · 2001
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Conditional self-attention for query-based summarization
Yujia Xie, Tianyi Zhou, Yi Mao, and Weizhu Chen. 2020 · 2002
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Textrank: Bringing order into text
Rada Mihalcea and Paul Tarau. 2004 · 2004
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Query focused multi-document summarization with distant supervision
Yumo Xu and Mirella Lapata. 2020c · 2004
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Overview of duc 2005
Hoa Trang Dang. 2005 · 2005
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Caire-covid: A question answering and multi-document summarization system for covid-19 research
Dan Su, Yan Xu, Tiezheng Yu, Farhad Bin Siddique, Elham J Barezi, and Pascale Fung. 2020 · 2005
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020 · 2005
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Bayesian query-focused summarization
Hal Daumé III and Daniel Marcu. 2006 · 2006
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Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, and Fernando Pereira. 2007 · 2007
Cited alongside, same era.
The new york times annotated corpus
Evan Sandhaus. 2008 · 2008
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Frustratingly easy domain adaptation
Hal Daumé III. 2009 · 2009
Cited alongside, same era.
Occams–an optimal combinatorial covering algorithm for multi-document summarization
Sashka T Davis, John M Conroy, and Judith D Schlesinger. 2012 · 2012
Cited alongside, same era.
Crossner: Evaluating cross-domain named entity recognition
Zihan Liu, Yan Xu, Tiezheng Yu, Wenliang Dai, Ziwei Ji, Samuel Cahyawijaya, Andrea Madotto, and Pascale Fung. 2020 · 2012
Cited alongside, same era.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
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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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Tal Baumel, Matan Eyal, and Michael Elhadad. 2018 · 2018
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Yumo Xu and Mirella Lapata. 2020a · 2012
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
Cited alongside, same era.
Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Cited alongside, same era.
Newsqa: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2016 · 2016
Cited alongside, same era.
Searchqa: A new q&a dataset augmented with context from a search engine
Matthew Dunn, Levent Sagun, Mike Higgins, V Ugur Guney, Volkan Cirik, and Kyunghyun Cho. 2017 · 2017
Cited alongside, same era.
Unsupervised query-focused multi-document summarization using the cross entropy method
Guy Feigenblat, Haggai Roitman, Odellia Boni, and David Konopnicki. 2017 · 2017
Cited alongside, same era.
A pilot study of domain adaptation effect for neural abstractive summarization
Xinyu Hua and Lu Wang. 2017 · 2017
Cited alongside, same era.
Sebastian Gehrmann, Yuntian Deng, and Alexander M Rush. 2018 · 2018
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Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies
Max Grusky, Mor Naaman, and Yoav Artzi. 2018 · 2018
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Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018 · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
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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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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, et al. 2019 · 2019
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Generalizing question answering system with pre-trained language model fine-tuning
Dan Su, Yan Xu, Genta Indra Winata, Peng Xu, Hyeondey Kim, Zihan Liu, and Pascale Fung. 2019 · 2019
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Query focused abstractive summarization via incorporating query relevance and transfer learning with transformer models
Md Tahmid Rahman Laskar, Enamul Hoque, and Jimmy Huang. 2020 · 2020
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Coarse-to-fine query focused multi-document summarization
Yumo Xu and Mirella Lapata. 2020b · 2020
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Adaptsum: Towards low-resource domain adaptation for abstractive summarization
Tiezheng Yu, Zihan Liu, and Pascale Fung. 2021 · 2021
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