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The Query Focused Text Summarization (QFTS) task aims at building systems that generate the summary of the text document(s) based on the given query.
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019a · 1904
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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. 2019b · 1907
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 1909
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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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HuggingFace’s Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, et al. 2019 · 1910
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Joint learning of answer selection and answer summary generation in community question answering
Yang Deng, Wai Lam, Yuexiang Xie, Daoyuan Chen, Yaliang Li, Min Yang, and Ying Shen. 2019 · 1911
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Tanda: Transfer and adapt pre-trained transformer models for answer sentence selection
Siddhant Garg, Thuy Vu, and Alessandro Moschitti. 2019 · 1911
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J Liu. 2019a · 1912
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Multi-passage machine reading comprehension with cross-passage answer verification
Yizhong Wang, Kai Liu, Jing Liu, Wei He, Yajuan Lyu, Hua Wu, Sujian Li, and Haifeng Wang. 2018 · 1927
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Uxla: A robust unsupervised data augmentation framework for zero-resource cross-lingual nlp
M Saiful Bari, Muhammad Tasnim Mohiuddin, and Shafiq Joty. 2021 · 1992
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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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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Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning. 2020 · 2003
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Pre-trained models for natural language processing: A survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang. 2020 · 2003
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Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020 · 2004
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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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
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York University at TREC 2005: Genomics track
Xiangji Huang, Ming Zhong, and Luo Si. 2005 · 2005
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Dan Su, Yan Xu, Tiezheng Yu, Farhad Bin Siddique, Elham J Barezi, and Pascale Fung. 2020 · 2005
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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z Li, Madian Khabsa, Han Fang, and Hao Ma. 2020 · 2006
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ARSA: a sentiment-aware model for predicting sales performance using blogs
Yang Liu, Xiangji Huang, Aijun An, and Xiaohui Yu. 2007 · 2007
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What is the jeopardy model? a quasi-synchronous grammar for qa
Mengqiu Wang, Noah A Smith, and Teruko Mitamura. 2007 · 2007
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Big bird: Transformers for longer sequences
Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al. 2020 · 2007
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Integrating clustering and multi-document summarization to improve document understanding
Dingding Wang, Shenghuo Zhu, Tao Li, Yun Chi, and Yihong Gong. 2008 · 2008
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Rethinking attention with performers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, et al. 2020 · 2009
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Exploring content models for multi-document summarization
Aria Haghighi and Lucy Vanderwende. 2009 · 2009
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A bayesian learning approach to promoting diversity in ranking for biomedical information retrieval
Xiangji Huang and Qinmin Hu. 2009 · 2009
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Efficient transformers: A survey
Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler. 2020 · 2009
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Graph-based multi-modality learning for topic-focused multi-document summarization
Xiaojun Wan and Jianguo Xiao. 2009 · 2009
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Aquamuse: Automatically generating datasets for query-based multi-document summarization
Sayali Kulkarni, Sheide Chammas, Wan Zhu, Fei Sha, and Eugene Ie. 2020 · 2010
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Deep learning for text style transfer: A survey
Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, and Rada Mihalcea. 2020 · 2011
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Utilizing bidirectional encoder representations from transformers for answer selection
Md Tahmid Rahman Laskar, Enamul Hoque, and Jimmy Xiangji Huang. 2020b · 2011
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Abstractive query focused summarization with query-free resources
Yumo Xu and Mirella Lapata. 2020a · 2012
Cited alongside, same era.
Mining online reviews for predicting sales performance: A case study in the movie domain
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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A gated self-attention memory network for answer selection
Tuan Lai, Quan Hung Tran, Trung Bui, and Daisuke Kihara. 2019 · 2019
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Abstractive multi-document summarization based on semantic link network
Wei Li and Hai Zhuge. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019b · 2019
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Multi-style generative reading comprehension
Kyosuke Nishida, Itsumi Saito, Kosuke Nishida, Kazutoshi Shinoda, Atsushi Otsuka, Hisako Asano, and Junji Tomita. 2019 · 2019
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To tune or not to tune? adapting pretrained representations to diverse tasks
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Xiaohui Yu, Yang Liu, Xiangji Huang, and Aijun An. 2012 · 2012
Cited alongside, same era.
Automatically assessing machine summary content without a gold standard
Annie Louis and Ani Nenkova. 2013 · 2013
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.
Ctsum: extracting more certain summaries for news articles
Xiaojun Wan and Jianmin Zhang. 2014 · 2014
Cited alongside, same era.
VQA: Visual Question Answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh. 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
Cited alongside, same era.
A neural attention model for abstractive sentence summarization
Alexander M Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Cited alongside, same era.
Compressive document summarization via sparse optimization
Jin-ge Yao, Xiaojun Wan, and Jianguo Xiao. 2015 · 2015
Cited alongside, same era.
Matthew E. Peters, Sebastian Ruder, and Noah A. Smith. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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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 · 2019
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Sum-qe: a bert-based summary quality estimation model
Stratos Xenouleas, Prodromos Malakasiotis, Marianna Apidianaki, and Ion Androutsopoulos. 2019 · 2019
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Towards generating query to perform query focused abstractive summarization using pre-trained model
Deen Mohammad Abdullah and Yllias Chali. 2020 · 2020
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Selection driven query focused abstractive document summarization
Chudamani Aryal and Yllias Chali. 2020 · 2020
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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
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Neural query-biased abstractive summarization using copying mechanism
Tatsuya Ishigaki, Hen-Hsen Huang, Hiroya Takamura, Hsin-Hsi Chen, and Manabu Okumura. 2020 · 2020
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Answering questions about charts and generating visual explanations
Dae Hyun Kim, Enamul Hoque, and Maneesh Agrawala. 2020 · 2020
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Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training
Weizhen Qi, Yu Yan, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, and Ming Zhou. 2020 · 2020
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Neural unsupervised domain adaptation in nlp—a survey
Alan Ramponi and Barbara Plank. 2020 · 2020
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Unsupervised dual-cascade learning with pseudo-feedback distillation for query-focused extractive summarization
Haggai Roitman, Guy Feigenblat, Doron Cohen, Odellia Boni, and David Konopnicki. 2020 · 2020
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Question-driven summarization of answers to consumer health questions
Max Savery, Asma Ben Abacha, Soumya Gayen, and Dina Demner-Fushman. 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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Improving zero and few-shot abstractive summarization with intermediate fine-tuning and data augmentation
Alexander Richard Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, and Yashar Mehdad. 2021 · 2021
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Improving punctuation restoration for speech transcripts via external data
Xue-Yong Fu, Cheng Chen, Md Tahmid Rahman Laskar, Shashi Bhushan, and Simon Corston-Oliver. 2021 · 2021
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Data augmentation for abstractive query-focused multi-document summarization
Ramakanth Pasunuru, Asli Celikyilmaz, Michel Galley, Chenyan Xiong, Yizhe Zhang, Mohit Bansal, and Jianfeng Gao. 2021 · 2021
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2021 · 2021
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Improve query focused abstractive summarization by incorporating answer relevance
Dan Su, Tiezheng Yu, and Pascale Fung. 2021 · 2021
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Exploring neural models for query-focused summarization
Jesse Vig, Alexander R Fabbri, and Wojciech Kryściński. 2021 · 2021
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Text summarization with latent queries
Yumo Xu and Mirella Lapata. 2021 · 2021
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Bartscore: Evaluating generated text as text generation
Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021 · 2021
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Qmsum: A new benchmark for query-based multi-domain meeting summarization
Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, et al. 2021 · 2021
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Dfm: A parameter-shared deep fused model for knowledge base question answering
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