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With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties.
Fine-tune bert for extractive summarization
Yang Liu. 2019 · 1903
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
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Topic-aware pointer-generator networks for summarizing spoken conversations
Zhengyuan Liu, Angela Ng, Sheldon Lee, Ai Ti Aw, and Nancy F Chen. 2019b · 1910
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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, and Peter J Liu. 2019 · 1910
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J Liu. 2019 · 1912
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Make lead bias in your favor: A simple and effective method for news summarization
Chenguang Zhu, Ziyi Yang, Robert Gmyr, Michael Zeng, and Xuedong Huang. 2019 · 1912
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Automatic dialogue summary generation for customer service
Chunyi Liu, Peng Wang, Jiang Xu, Zang Li, and Jieping Ye. 2019a · 1965
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The icsi meeting corpus
Adam Janin, Don Baron, Jane Edwards, Dan Ellis, David Gelbart, Nelson Morgan, Barbara Peskin, Thilo Pfau, Elizabeth Shriberg, Andreas Stolcke, et al. 2003 · 2003
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Textrank: Bringing order into text
Rada Mihalcea and Paul Tarau. 2004 · 2004
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The ami meeting corpus
Iain McCowan, Jean Carletta, Wessel Kraaij, Simone Ashby, S Bourban, M Flynn, M Guillemot, Thomas Hain, J Kadlec, Vasilis Karaiskos, et al. 2005 · 2005
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Packing the meeting summarization knapsack
Korbinian Riedhammer, Dan Gillick, Benoit Favre, and Dilek Hakkani-Tür. 2008 · 2008
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The new york times annotated corpus
Evan Sandhaus. 2008 · 2008
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Clusterrank: a graph based method for meeting summarization
Nikhil Garg, Benoit Favre, Korbinian Reidhammer, and Dilek Hakkani-Tür. 2009 · 2009
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Generating and validating abstracts of meeting conversations: a user study
Gabriel Murray, Giuseppe Carenini, and Raymond Ng. 2010 · 2010
Cited alongside, same era.
Integrating intra-speaker topic modeling and temporal-based inter-speaker topic modeling in random walk for improved multi-party meeting summarization
Yun-Nung Chen and Florian Metze. 2012 · 2012
Cited alongside, same era.
Abstractive meeting summarization with entailment and fusion
Yashar Mehdad, Giuseppe Carenini, Frank Tompa, et al. 2013 · 2013
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Domain-independent abstract generation for focused meeting summarization
Lu Wang and Claire Cardie. 2013 · 2013
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A template-based abstractive meeting summarization: Leveraging summary and source text relationships
Tatsuro Oya, Yashar Mehdad, Giuseppe Carenini, and Raymond Ng. 2014 · 2014
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Building end-to-end dialogue systems using generative hierarchical neural network models
Iulian V Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau. 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander M Rush. 2018 · 2018
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Cited alongside, same era.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Cited alongside, same era.
An improved non-monotonic transition system for dependency parsing
Matthew Honnibal and Mark Johnson. 2015 · 2015
Cited alongside, same era.
A hierarchical neural autoencoder for paragraphs and documents
Jiwei Li, Minh-Thang Luong, and Dan Jurafsky. 2015 · 2015
Cited alongside, same era.
Hierarchical recurrent neural network for document modeling
Rui Lin, Shujie Liu, Muyun Yang, Mu Li, Ming Zhou, and Sheng Li. 2015 · 2015
Cited alongside, same era.
A neural attention model for abstractive sentence summarization
Alexander M Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Cited alongside, same era.
Learning user and product distributed representations using a sequence model for sentiment analysis
Tao Chen, Ruifeng Xu, Yulan He, Yunqing Xia, and Xuan Wang. 2016 · 2016
Cited alongside, same era.
Closed-book training to improve summarization encoder memory
Yichen Jiang and Mohit Bansal. 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
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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
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Unsupervised abstractive meeting summarization with multi-sentence compression and budgeted submodular maximization
Guokan Shang, Wensi Ding, Zekun Zhang, Antoine Tixier, Polykarpos Meladianos, Michalis Vazirgiannis, and Jean-Pierre Lorré. 2018 · 2018
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Keep meeting summaries on topic: Abstractive multi-modal meeting summarization
Manling Li, Lingyu Zhang, Heng Ji, and Richard J Radke. 2019 · 2019
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Reading turn by turn: Hierarchical attention architecture for spoken dialogue comprehension
Zhengyuan Liu and Nancy Chen. 2019 · 2019
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Abstractive meeting summarization via hierarchical adaptive segmental network learning
Zhou Zhao, Haojie Pan, Changjie Fan, Yan Liu, Linlin Li, Min Yang, and Deng Cai. 2019 · 2019
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On the variance of the adaptive learning rate and beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han. 2020 · 2020
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