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Commonly adopted metrics for extractive summarization focus on lexical overlap at the token level.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019b · 1904
Earlier work this paper cites.
Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J Liu. 2019a · 1912
Earlier work this paper cites.
Better summarization evaluation with word embeddings for ROUGE
Jun-Ping Ng and Viktoria Abrecht. 2015 · 1930
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Evaluating content selection in summarization: The pyramid method
Ani Nenkova and Rebecca Passonneau. 2004 · 2004
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An introduction to duc-2004
Over Paul and Yen James. 2004 · 2004
Earlier work this paper cites.
Overview of the tac 2008 update summarization task
Hoa Trang Dang and Karolina Owczarzak. 2008 · 2008
Earlier work this paper cites.
A thorough examination of the CNN/daily mail reading comprehension task
Danqi Chen, Jason Bolton, and Christopher D. Manning. 2016 · 2016
Earlier work this paper cites.
Abstractive text summarization using sequence-to-sequence RNNs and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Çağlar Gu̇lçehre, and Bing Xiang. 2016 · 2016
Earlier work this paper cites.
Peak: Pyramid evaluation via automated knowledge extraction
Qian Yang, Rebecca J Passonneau, and Gerard De Melo. 2016 · 2016
Earlier work this paper cites.
Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017 · 2017
Earlier work this paper cites.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
Earlier work this paper cites.
The limits of automatic summarisation according to ROUGE
Natalie Schluter. 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.
Fast abstractive summarization with reinforce-selected sentence rewriting
Yen-Chun Chen and Mohit Bansal. 2018 · 2018
Cited alongside, same era.
BanditSum: Extractive summarization as a contextual bandit
Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, and Jackie Chi Kit Cheung. 2018 · 2018
Cited alongside, same era.
Rouge 2.0: Updated and improved measures for evaluation of summarization tasks
Kavita Ganesan. 2018 · 2018
Cited alongside, same era.
A unified model for extractive and abstractive summarization using inconsistency loss
What it takes to achieve 100% condition accuracy on WikiSQL
Semih Yavuz, Izzeddin Gur, Yu Su, and Xifeng Yan. 2018 · 2018
Later among the works it cites.
Neural document summarization by jointly learning to score and select sentences
Qingyu Zhou, Nan Yang, Furu Wei, Shaohan Huang, Ming Zhou, and Tiejun Zhao. 2018 · 2018
Later among the works it cites.
Which scores to predict in sentence regression for text summarization?
Markus Zopf, Eneldo Loza Mencía, and Johannes Fürnkranz. 2018 · 2018
Later among the works it cites.
HighRES: Highlight-based reference-less evaluation of summarization
Hardy Hardy, Shashi Narayan, and Andreas Vlachos. 2019 · 2019
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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
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Neural text summarization: A critical evaluation
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Wan-Ting Hsu, Chieh-Kai Lin, Ming-Ying Lee, Kerui Min, Jing Tang, and Min Sun. 2018 · 2018
Cited alongside, same era.
Content selection in deep learning models of summarization
Chris Kedzie, Kathleen McKeown, and Hal Daumé III. 2018 · 2018
Cited alongside, same era.
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. 2018b · 2018
Cited alongside, same era.
Ranking sentences for extractive summarization with reinforcement learning
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018c · 2018
Cited alongside, same era.
A graph-theoretic summary evaluation for ROUGE
Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, and Fang Chen. 2018 · 2018
Cited alongside, same era.
Evaluating multiple system summary lengths: A case study
Ori Shapira, David Gabay, Hadar Ronen, Judit Bar-Ilan, Yael Amsterdamer, Ani Nenkova, and Ido Dagan. 2018 · 2018
Cited alongside, same era.
Wojciech Kryscinski, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
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The feasibility of embedding based automatic evaluation for single document summarization
Simeng Sun and Ani Nenkova. 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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MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance
Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M. Meyer, and Steffen Eger. 2019 · 2019
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Generating representative headlines for news stories
Xiaotao Gu, Yuning Mao, Jiawei Han, Jialu Liu, Hongkun Yu, You Wu, Cong Yu, Daniel Finnie, Jiaqi Zhai, and Nicholas Zukoski. 2020 · 2020
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Document modeling with external attention for sentence extraction
Shashi Narayan, Ronald Cardenas, Nikos Papasarantopoulos, Shay B. Cohen, Mirella Lapata, Jiangsheng Yu, and Yi Chang. 2018a · 2030
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