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Automatic text summarization has enjoyed great progress over the years and is used in numerous applications, impacting the lives of many.
Automatic text summarization: What has been done and what has to be done
Abdelkrime Aries, Djamel Eddine Zegour, and Walid-Khaled Hidouci. 2019 · 1904
Earlier work this paper cites.
Screenplay summarization using latent narrative structure
Pinelopi Papalampidi, Frank Keller, Lea Frermann, and Mirella Lapata. 2020 · 1933
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Unsupervised opinion summarization with noising and denoising
Reinald Kim Amplayo and Mirella Lapata. 2020 · 1945
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LCSTS: A large scale Chinese short text summarization dataset
Baotian Hu, Qingcai Chen, and Fangze Zhu. 2015 · 1972
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A comparison of texts and their summaries: Memorial consequences
Lynne M Reder and John R Anderson. 1980 · 1980
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Automatic summarizing: factors and directions
Karen Spärck Jones. 1998 · 1998
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Summarization evaluation: An overview
Inderjeet Mani. 2001b · 2001
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The IIR evaluation model: a framework for evaluation of interactive information retrieval systems
Pia Borlund. 2003 · 2003
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Duc 2003: Documents, tasks, and measures
DUC. 2003 · 2003
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Evaluating content selection in summarization: The pyramid method
Ani Nenkova and Rebecca Passonneau. 2004 · 2004
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A methodology for extrinsic evaluation of text summarization: Does ROUGE correlate?
Bonnie Dorr, Christof Monz, Stacy President, Richard Schwartz, and David Zajic. 2005 · 2005
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The New York Times annotated corpus
Evan Sandhaus. 2008 · 2008
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Extracting summary knowledge graphs from long documents
Zeqiu Wu, Rik Koncel-Kedziorski, Mari Ostendorf, and Hannaneh Hajishirzi. 2020 · 2009
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Modeling the impact of short- and long-term behavior on search personalization
Paul N. Bennett, Ryen W. White, Wei Chu, Susan T. Dumais, Peter Bailey, Fedor Borisyuk, and Xiaoyuan Cui. 2012 · 2012
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Decoding learning: The proof, promise and potential of digital education
Rosemary Luckin, Brett Bligh, Andrew Manches, Shaaron Ainsworth, Charles Crook, and Richard Noss. 2012 · 2012
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Personalized text summarization based on important terms identification
Róbert Móro and Mária Bieliková. 2012 · 2012
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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The SENSEI project: Making sense of human conversations
Giuseppe Riccardi, Frédéric Béchet, Morena Danieli, Benoît Favre, Robert J. Gaizauskas, Udo Kruschwitz, and Massimo Poesio. 2015 · 2015
Earlier work this paper cites.
A neural attention model for abstractive sentence summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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A study of the use of simulated work task situations in interactive information retrieval evaluations: A meta-evaluation
Pia Borlund. 2016 · 2016
Cited alongside, same era.
Neural summarization by extracting sentences and words
Jianpeng Cheng and Mirella Lapata. 2016 · 2016
Cited alongside, same era.
Abstractive sentence summarization with attentive recurrent neural networks
Sumit Chopra, Michael Auli, and Alexander M. Rush. 2016 · 2016
Cited alongside, same era.
A sentence compression based framework to query-focused multi-document summarization
Lu Wang, Hema Raghavan, Vittorio Castelli, Radu Florian, and Claire Cardie. 2016 · 2016
Cited alongside, same era.
Bringing structure into summaries: Crowdsourcing a benchmark corpus of concept maps
Tobias Falke and Iryna Gurevych. 2017 · 2017
Cited alongside, same era.
Ranking sentences for extractive summarization with reinforcement learning
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018b · 2018
Later among the works it cites.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
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Teacher educators’ use of digital tools and needs for digital competence in higher education
Lisbeth Amhag, Lisa Hellström, and Martin Stigmar. 2019 · 2019
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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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Assessing the factual accuracy of generated text
Ben Goodrich, Vinay Rao, Peter J. Liu, and Mohammad Saleh. 2019 · 2019
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Text summarization with pretrained encoders
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Query-based summarization using MDL principle
Marina Litvak and Natalia Vanetik. 2017 · 2017
Cited alongside, same era.
Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017 · 2017
Cited alongside, same era.
Diversity driven attention model for query-based abstractive summarization
Preksha Nema, Mitesh M. Khapra, Anirban Laha, and Balaraman Ravindran. 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.
Abstractive document summarization with a graph-based attentional neural model
Jiwei Tan, Xiaojun Wan, and Jianguo Xiao. 2017 · 2017
Cited alongside, same era.
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
Cited alongside, same era.
TL;DR: Mining Reddit to learn automatic summarization
Michael Völske, Martin Potthast, Shahbaz Syed, and Benno Stein. 2017 · 2017
Cited alongside, same era.
Yang Liu and Mirella Lapata. 2019 · 2019
Later among the works it cites.
Multi-hop inference for question-driven summarization
Yang Deng, Wenxuan Zhang, and Wai Lam. 2020 · 2020
Closest in time.
FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
Esin Durmus, He He, and Mona Diab. 2020 · 2020
Closest in time.
WikiLingua: A new benchmark dataset for cross-lingual abstractive summarization
Faisal Ladhak, Esin Durmus, Claire Cardie, and Kathleen McKeown. 2020 · 2020
Closest in time.
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
Closest in time.
Conversations with documents: An exploration of document-centered assistance
Maartje ter Hoeve, Robert Sim, Elnaz Nouri, Adam Fourney, Maarten de Rijke, and Ryen W. White. 2020 · 2020
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Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2020
Closest in time.
Discourse-aware neural extractive text summarization
Jiacheng Xu, Zhe Gan, Yu Cheng, and Jingjing Liu. 2020 · 2020
Closest in time.
Summarizing and exploring tabular data in conversational search
Shuo Zhang, Zhuyun Dai, Krisztian Balog, and Jamie Callan. 2020a · 2020
Closest in time.
Bertscore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020b · 2020
Closest in time.
iFacetSum: Coreference-based interactive faceted summarization for multi-document exploration
Eran Hirsch, Alon Eirew, Ori Shapira, Avi Caciularu, Arie Cattan, Ori Ernst, Ramakanth Pasunuru, Hadar Ronen, Mohit Bansal, and Ido Dagan. 2021 · 2021
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Topic-aware contrastive learning for abstractive dialogue summarization
Junpeng Liu, Yanyan Zou, Hainan Zhang, Hongshen Chen, Zhuoye Ding, Caixia Yuan, and Xiaojie Wang. 2021 · 2021
Closest in time.
Bringing structure into summaries: a faceted summarization dataset for long scientific documents
Rui Meng, Khushboo Thaker, Lei Zhang, Yue Dong, Xingdi Yuan, Tong Wang, and Daqing He. 2021 · 2021
Closest in time.
Factual consistency evaluation for text summarization via counterfactual estimation
Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, and Bolin Ding. 2021 · 2021
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