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Transformer-based language models usually treat texts as linear sequences.
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. 2019 · 1907
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LAMBERT: layout-aware language modeling using BERT for information extraction
Lukasz Garncarek, Rafal Powalski, Tomasz Stanislawek, Bartosz Topolski, Piotr Halama, and Filip Gralinski. 2020 · 2002
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Longformer: The long-document transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020 · 2004
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A divide-and-conquer approach to the summarization of academic articles
Alexios Gidiotis and Grigorios Tsoumakas. 2020 · 2004
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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The Stanford CoreNLP natural language processing toolkit
Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014 · 2014
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Neural summarization by extracting sentences and words
Jianpeng Cheng and Mirella Lapata. 2016 · 2016
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Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 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, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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A discourse-aware attention model for abstractive summarization of long documents
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018 · 2018
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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
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Extractive summarization of long documents by combining global and local context
Aspect-based document similarity for research papers
Malte Ostendorff, Terry Ruas, Till Blume, Bela Gipp, and Georg Rehm. 2020 · 2020
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On extractive and abstractive neural document summarization with transformer language models
Jonathan Pilault, Raymond Li, Sandeep Subramanian, and Chris Pal. 2020 · 2020
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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. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Systematically exploring redundancy reduction in summarizing long documents
Wen Xiao and Giuseppe Carenini. 2020 · 2020
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Wen Xiao and Giuseppe Carenini. 2019 · 2019
Cited alongside, same era.
HIBERT: Document level pre-training of hierarchical bidirectional transformers for document summarization
Xingxing Zhang, Furu Wei, and Ming Zhou. 2019 · 2019
Cited alongside, same era.
Abstractive text summarization based on language model conditioning and locality modeling
Dmitrii Aksenov, Julian Moreno-Schneider, Peter Bourgonje, Robert Schwarzenberg, Leonhard Hennig, and Georg Rehm. 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.
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Layoutlm: Pre-training of text and layout for document image understanding
Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. 2020 · 2020
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Big bird: Transformers for longer sequences
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontañón, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, and Amr Ahmed. 2020 · 2020
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PEGASUS: pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2020 · 2020
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Extractive summarization as text matching
Ming Zhong, Pengfei Liu, Yiran Chen, Danqing Wang, Xipeng Qiu, and Xuanjing Huang. 2020 · 2020
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