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In news articles the lead bias is a common phenomenon that usually dominates the learning signals for neural extractive summarizers, severely limiting their performance on data with different or even no bias.
Exploring domain shift in extractive text summarization
Danqing Wang, Pengfei Liu, Ming Zhong, Jie Fu, Xipeng Qiu, and Xuanjing Huang. 2019 · 1908
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Automatic summarization
Ani Nenkova, Sameer Maskey, and Yang Liu. 2011 · 2011
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Improving the estimation of word importance for news multi-document summarization
Kai Hong and Ani Nenkova. 2014 · 2014
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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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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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
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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Content selection in deep learning models of summarization
Chris Kedzie, Kathleen McKeown, and Hal Daumé III. 2018 · 2018
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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
Cited alongside, same era.
A span selection model for semantic role labeling
Hiroki Ouchi, Hiroyuki Shindo, and Yuji Matsumoto. 2018 · 2018
Cited alongside, same era.
Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
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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Topics to avoid: Demoting latent confounds in text classification
Sachin Kumar, Shuly Wintner, Noah A. Smith, and Yulia Tsvetkov. 2019 · 2019
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Extractive summarization of long documents by combining global and local context
Wen Xiao and Giuseppe Carenini. 2019 · 2019
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Learning to model and ignore dataset bias with mixed capacity ensembles
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 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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Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2019 · 2019
Cited alongside, same era.
Countering the effects of lead bias in news summarization via multi-stage training and auxiliary losses
Matt Grenander, Yue Dong, Jackie Chi Kit Cheung, and Annie Louis. 2019 · 2019
Cited alongside, same era.
Searching for effective neural extractive summarization: What works and what’s next
Ming Zhong, Pengfei Liu, Danqing Wang, Xipeng Qiu, and Xuanjing Huang. 2019a
Cited in the paper.
A closer look at data bias in neural extractive summarization models
Ming Zhong, Danqing Wang, Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2019b
Cited in the paper.
Wen Xiao, Patrick Huber, and Giuseppe Carenini. 2020 · 2020
Later among the works it cites.
Improving context modeling in neural topic segmentation
Linzi Xing, Brad Hackinen, Giuseppe Carenini, and Francesco Trebbi. 2020 · 2020
Later among the works it cites.