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Novel neural architectures, training strategies, and the availability of large-scale corpora haven been the driving force behind recent progress in abstractive text summarization.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Summeval: Re-evaluating summarization evaluation
Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir R. Radev. 2020 · 2007
Earlier work this paper cites.
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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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan. 2017 · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies
Max Grusky, Mor Naaman, and Yoav Artzi. 2018 · 2018
Earlier work this paper cites.
Content selection in deep learning models of summarization
Chris Kedzie, Kathleen R. McKeown, and Hal Daumé III. 2018 · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model
Alexander Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir Radev. 2019 · 2019
Earlier work this paper cites.
Visual interaction with deep learning models through collaborative semantic inference
Sebastian Gehrmann, Hendrik Strobelt, Robert Krüger, Hanspeter Pfister, and Alexander M Rush. 2019 · 2019
Cited alongside, same era.
Earlier isn’t always better: Sub-aspect analysis on corpus and system biases in summarization
Taehee Jung, Dongyeop Kang, Lucas Mentch, and Eduard Hovy. 2019 · 2019
Cited alongside, same era.
Neural text summarization: A critical evaluation
Wojciech Kryściński, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
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
Later among the works it cites.
The language interpretability tool: Extensible, interactive visualizations and analysis for NLP models
Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, and Ann Yuan. 2020 · 2020
Later among the works it cites.
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, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020a · 2020
Later among the works it cites.
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
Later among the works it cites.
PEGASUS: pre-training with extracted gap-sentences for abstractive summarization
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Cited alongside, same era.
Seq2seq-vis: A visual debugging tool for sequence-to-sequence models
Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, and Alexander M. Rush. 2019 · 2019
Cited alongside, same era.
A closer look at data bias in neural extractive summarization models
Ming Zhong, Danqing Wang, Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2019 · 2019
Cited alongside, same era.
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Cited alongside, same era.
spaCy: Industrial-strength Natural Language Processing in Python
Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Adriane Boyd. 2020 · 2020
Cited alongside, same era.
Evaluating the factual consistency of abstractive text summarization
Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher. 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.
Datasets
Thomas Wolf, Quentin Lhoest, Patrick von Platen, Yacine Jernite, Mariama Drame, Julien Plu, Julien Chaumond, Clement Delangue, Clara Ma, Abhishek Thakur, Suraj Patil, Joe Davison, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angie McMillan-Major, Simon Brandeis, Sylvain Gugger, François Lagunas, Lysandre Debut, Morgan Funtowicz, Anthony Moi, Sasha Rush, Philipp Schmidd, Pierric Cistac, Victor Muštar, Jeff Boudier, and Anna Tordjmann. 2020b
Cited in the paper.
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2020a · 2020
Later among the works it cites.
Bertscore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020b · 2020
Later among the works it cites.
The gem benchmark: Natural language generation, its evaluation and metrics
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, Pawan Sasanka Ammanamanchi, Aremu Anuoluwapo, Antoine Bosselut, Khyathi Raghavi Chandu, Miruna Clinciu, Dipanjan Das, Kaustubh D Dhole, et al. 2021 · 2021
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Robustness Gym: Unifying the NLP evaluation landscape
Karan Goel, Nazneen Rajani, Jesse Vig, Zachary Taschdjian, Mohit Bansal, and Christopher Ré. 2021 · 2021
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Genie: A leaderboard for human-in-the-loop evaluation of text generation
Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A Smith, and Daniel S Weld. 2021 · 2021
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How to evaluate a summarizer: Study design and statistical analysis for manual linguistic quality evaluation
Julius Steen and Katja Markert. 2021 · 2021
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A survey of text alignment visualization
T. Yousef and S. Janicke. 2021 · 2021
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