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Controllable summarization allows users to generate customized summaries with specified attributes.
Huggingface’s transformers: State-of-the-art natural language processing
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Length-controllable abstractive summarization by guiding with summary prototype
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iNeATS: Interactive multi-document summarization
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Lexrank: Graph-based lexical centrality as salience in text summarization
Günes Erkan and Dragomir R Radev. 2004 · 2004
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Overview of duc 2005
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Bayesian query-focused summarization
Hal Daumé III and Daniel Marcu. 2006 · 2006
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Query-focused summarization by supervised sentence ranking and skewed word distributions
Seeger Fisher and Brian Roark. 2006 · 2006
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Text specificity and impact on quality of news summaries
Annie Louis and Ani Nenkova. 2011 · 2011
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Ctrlsum: Towards generic controllable text summarization
Junxian He, Wojciech Kryściński, Bryan McCann, Nazneen Rajani, and Caiming Xiong. 2020 · 2012
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Teaching machines to read and comprehend
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A neural attention model for abstractive sentence summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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pke: an open source python-based keyphrase extraction toolkit
Florian Boudin. 2016 · 2016
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Neural summarization by extracting sentences and words
Jianpeng Cheng and Mirella Lapata. 2016 · 2016
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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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Controllable abstractive summarization
Angela Fan, David Grangier, and Michael Auli. 2018 · 2018
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Controlling length in abstractive summarization using a convolutional neural network
Yizhu Liu, Zhiyi Luo, and Kenny Zhu. 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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Better rewards yield better summaries: Learning to summarise without references
Florian Böhm, Yang Gao, Christian M. Meyer, Ori Shapira, Ido Dagan, and Iryna Gurevych. 2019 · 2019
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Global optimization under length constraint for neural text summarization
Takuya Makino, Tomoya Iwakura, Hiroya Takamura, and Manabu Okumura. 2019 · 2019
GSum: A general framework for guided neural abstractive summarization
Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi, Zhengbao Jiang, and Graham Neubig. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021 · 2021
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Planning with learned entity prompts for abstractive summarization
Shashi Narayan, Yao Zhao, Joshua Maynez, Gonçalo Simões, Vitaly Nikolaev, and Ryan McDonald. 2021 · 2021
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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, 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
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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
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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, Peter J Liu, et al. 2020 · 2020
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Control, generate, augment: A scalable framework for multi-attribute text generation
Giuseppe Russo, Nora Hollenstein, Claudiu Cristian Musat, and Ce Zhang. 2020 · 2020
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Eliciting knowledge from language models using automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
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Controlling the amount of verbatim copying in abstractive summarization
Kaiqiang Song, Bingqing Wang, Zhe Feng, Ren Liu, and Fei Liu. 2020 · 2020
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Guanghui Qin and Jason Eisner. 2021 · 2021
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 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 Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, and Dragomir Radev. 2021 · 2021
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AdaPrompt: Adaptive model training for prompt-based NLP
Yulong Chen, Yang Liu, Li Dong, Shuohang Wang, Chenguang Zhu, Michael Zeng, and Yue Zhang. 2022 · 2022
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HydraSum: Disentangling style features in text summarization with multi-decoder models
Tanya Goyal, Nazneen Rajani, Wenhao Liu, and Wojciech Kryscinski. 2022b · 2022
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Z-code++: A pre-trained language model optimized for abstractive summarization
Pengcheng He, Baolin Peng, Liyang Lu, Song Wang, Jie Mei, Yang Liu, Ruochen Xu, Hany Hassan Awadalla, Yu Shi, Chenguang Zhu, et al. 2022 · 2022
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Extractive entity-centric summarization as sentence selection using bi-encoders
Ella Hofmann-Coyle, Mayank Kulkarni, Lingjue Xie, Mounica Maddela, and Daniel Preotiuc-Pietro. 2022 · 2022
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Length control in abstractive summarization by pretraining information selection
Yizhu Liu, Qi Jia, and Kenny Zhu. 2022 · 2022
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EntSUM: A data set for entity-centric extractive summarization
Mounica Maddela, Mayank Kulkarni, and Daniel Preotiuc-Pietro. 2022 · 2022
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Noisy channel language model prompting for few-shot text classification
Sewon Min, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Attend to the right context: A plug-and-play module for content-controllable summarization
Wen Xiao, Lesly Miculicich, Yang Liu, Pengcheng He, and Giuseppe Carenini. 2022 · 2022
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Unsupervised summarization with customized granularities
Ming Zhong, Yang Liu, Suyu Ge, Yuning Mao, Yizhu Jiao, Xingxing Zhang, Yichong Xu, Chenguang Zhu, Michael Zeng, and Jiawei Han. 2022 · 2022
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