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Compared to news and chat summarization, the development of meeting summarization is hugely decelerated by the limited data.
Statistical models for text segmentation
Doug Beeferman, Adam Berger, and John Lafferty. 1999 · 1999
Earlier work this paper cites.
A critique and improvement of an evaluation metric for text segmentation
Lev Pevzner and Marti A Hearst. 2002 · 2002
Earlier work this paper cites.
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
Earlier work this paper cites.
Longformer: The long-document transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020 · 2004
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
The ami meeting corpus
I McCowan, J Carletta, W Kraaij, S Ashby, S Bourban, M Flynn, M Guillemot, T Hain, J Kadlec, V Karaiskos, et al. 2005 · 2005
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Caglar Gulcehre, and Bing Xiang. 2016 · 2016
Earlier work this paper cites.
Dailydialog: A manually labelled multi-turn dialogue dataset
Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017 · 2017
Earlier work this paper cites.
Content selection in deep learning models of summarization
Chris Kedzie, Kathleen Mckeown, and Hal Daumé III. 2018 · 2018
Earlier work this paper cites.
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.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Samsum corpus: A human-annotated dialogue dataset for abstractive summarization
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019 · 2019
Cited alongside, same era.
Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
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Do neural dialog systems use the conversation history effectively? an empirical study
Chinnadhurai Sankar, Sandeep Subramanian, Christopher Pal, Sarath Chandar, and Yoshua Bengio. 2019 · 2019
Cited alongside, same era.
A hierarchical network for abstractive meeting summarization with cross-domain pretraining
Chenguang Zhu, Ruochen Xu, Michael Zeng, and Xuedong Huang. 2020 · 2020
Later among the works it cites.
Dialogsum: A real-life scenario dialogue summarization dataset
Yulong Chen, Yang Liu, Liang Chen, and Yue Zhang. 2021 · 2021
Later among the works it cites.
Language model as an annotator: Exploring dialogpt for dialogue summarization
Xiachong Feng, Xiaocheng Feng, Libo Qin, Bing Qin, and Ting Liu. 2021 · 2021
Later among the works it cites.
Planning with learned entity prompts for abstractive summarization
Shashi Narayan, Yao Zhao, Joshua Maynez, Gonçalo Simões, Vitaly Nikolaev, and Ryan McDonald. 2021 · 2021
Later among the works it cites.
Emailsum: Abstractive email thread summarization
Shiyue Zhang, Asli Celikyilmaz, Jianfeng Gao, and Mohit Bansal. 2021 · 2021
Later among the works it cites.
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Multi-view sequence-to-sequence models with conversational structure for abstractive dialogue summarization
Jiaao Chen and Diyi Yang. 2020 · 2020
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A divide-and-conquer approach to the summarization of long documents
Alexios Gidiotis and Grigorios Tsoumakas. 2020 · 2020
Cited alongside, same era.
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
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Clts: A new chinese long text summarization dataset
Xiaojun Liu, Chuang Zhang, Xiaojun Chen, Yanan Cao, and Jinpeng Li. 2020 · 2020
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020 · 2020
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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. 2022a
Cited in the paper.
Dialoglm: Pre-trained model for long dialogue understanding and summarization
Ming Zhong, Yang Liu, Yichong Xu, Chenguang Zhu, and Michael Zeng. 2022b
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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, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, et al. 2021 · 2021
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Mediasum: A large-scale media interview dataset for dialogue summarization
Chenguang Zhu, Yang Liu, Jie Mei, and Michael Zeng. 2021 · 2021
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End-to-end segmentation-based news summarization
Yang Liu, Chenguang Zhu, and Michael Zeng. 2022 · 2022
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Dyle: Dynamic latent extraction for abstractive long-input summarization
Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed Awadallah, and Dragomir Radev. 2022 · 2022
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