Fetching the paper…
Reading the bibliography…
The availability of large-scale datasets has driven the development of neural models that create summaries from single documents, for generic purposes.
Transforming wikipedia into augmented data for query-focused summarization
Haichao Zhu, Li Dong, Furu Wei, Bing Qin, and Ting Liu. 2019 · 1911
Earlier work this paper cites.
Nonlinear programming
HW Kuhn, AW Tucker, et al. 1951 · 1951
Earlier work this paper cites.
Overview of duc 2005
Hoa Trang Dang. 2005 · 2005
Earlier work this paper cites.
DUC 2005: Evaluation of question-focused summarization systems
Hoa Trang Dang. 2006 · 2005
Earlier work this paper cites.
Automatically constructing a corpus of sentential paraphrases
William B Dolan and Chris Brockett. 2005 · 2005
Earlier work this paper cites.
Overview of duc 2006
TD Hoa. 2006 · 2006
Earlier work this paper cites.
Manifold-ranking based topic-focused multi-document summarization
Xiaojun Wan, Jianwu Yang, and Jianguo Xiao. 2007 · 2007
Earlier work this paper cites.
Using only cross-document relationships for both generic and topic-focused multi-document summarizations
Xiaojun Wan. 2008 · 2008
Earlier work this paper cites.
GSum: A general framework for guided neural abstractive summarization
Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi, Zhengbao Jiang, and Graham Neubig. 2020 · 2010
Earlier work this paper cites.
Improving query focused summarization using look-ahead strategy
Rama Badrinath, Suresh Venkatasubramaniyan, and CE Veni Madhavan. 2011 · 2011
Earlier work this paper cites.
Abstractive query focused summarization with query-free resources
Yumo Xu and Mirella Lapata. 2020a · 2012
Earlier work this paper cites.
CTSUM: extracting more certain summaries for news articles
Xiaojun Wan and Jianmin Zhang. 2014 · 2014
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.
A neural attention model for abstractive sentence summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Earlier work this paper cites.
Training very deep networks
Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. 2015 · 2015
Earlier work this paper cites.
MS MARCO: A human generated machine reading comprehension dataset
Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, et al. 2016 · 2016
Cited alongside, same era.
Topic concentration in query focused summarization datasets
Tal Baumel, Raphael Cohen, and Michael Elhadad. 2016 · 2016
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2016 · 2016
Cited alongside, same era.
Abstractive text summarization using sequence-to-sequence RNNs and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang. 2016 · 2016
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.
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.
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
Later among the works it cites.
Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019b · 2019
Later among the works it cites.
Generating summaries with topic templates and structured convolutional decoders
Laura Perez-Beltrachini, Yang Liu, and Mirella Lapata. 2019 · 2019
Later among the works it cites.
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
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
I. Higgins, Loïc Matthey, A. Pal, Christopher P. Burgess, Xavier Glorot, M. Botvinick, S. Mohamed, and Alexander Lerchner. 2017 · 2017
Cited alongside, same era.
Diversity driven attention model for query-based abstractive summarization
Preksha Nema, Mitesh M. Khapra, Anirban Laha, and Balaraman Ravindran. 2017 · 2017
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, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Tal Baumel, Matan Eyal, and Michael Elhadad. 2018 · 2018
Cited alongside, same era.
Retrieve, rerank and rewrite: Soft template based neural summarization
Ziqiang Cao, Wenjie Li, Sujian Li, and Furu Wei. 2018 · 2018
Cited alongside, same era.
Towards generating query to perform query focused abstractive summarization using pre-trained model
Deen Mohammad Abdullah and Yllias Chali. 2020 · 2020
Later among the works it cites.
BioMedBERT: A pre-trained biomedical language model for qa and ir
Souradip Chakraborty, Ekaba Bisong, Shweta Bhatt, Thomas Wagner, Riley Elliott, and Francesco Mosconi. 2020 · 2020
Later among the works it cites.
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
Later among the works it cites.
CAiRE-COVID: A question answering and query-focused multi-document summarization system for COVID-19 scholarly information management
Dan Su, Yan Xu, Tiezheng Yu, Farhad Bin Siddique, Elham Barezi, and Pascale Fung. 2020 · 2020
Later among the works it cites.
Friendly topic assistant for transformer based abstractive summarization
Zhengjue Wang, Zhibin Duan, Hao Zhang, Chaojie Wang, Long Tian, Bo Chen, and Mingyuan Zhou. 2020 · 2020
Later among the works it cites.
Coarse-to-fine query focused multi-document summarization
Yumo Xu and Mirella Lapata. 2020b · 2020
Later among the works it cites.
Extractive summarization as text matching
Ming Zhong, Pengfei Liu, Yiran Chen, Danqing Wang, Xipeng Qiu, and Xuanjing Huang. 2020 · 2020
Later among the works it cites.
Enhancing factual consistency of abstractive summarization
Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, and Meng Jiang. 2021 · 2021
Closest in time.
Cascaded attention based unsupervised information distillation for compressive summarization
Piji Li, Wai Lam, Lidong Bing, Weiwei Guo, and Hang Li. 2017a · 2090
Closest in time.