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Transcripts generated by automatic speech recognition (ASR) systems for spoken documents lack structural annotations such as paragraphs, significantly reducing their readability.
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Marti A. Hearst, · 1994
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“Discourse segmentation in aid of document summarization,”
Branimir Boguraev and Mary S. Neff, · 2000
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“Advances in domain independent linear text segmentation,”
Freddy Y. Y. Choi, · 2000
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“Topic-based document segmentation with probabilistic latent semantic analysis,”
Thorsten Brants, Francine Chen, and Ioannis Tsochantaridis, · 2002
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“Measuring the readability of automatic speech-to-text transcripts,”
Douglas A. Jones, Florian Wolf, Edward Gibson, Elliott Williams, Evelina Fedorenko, Douglas A. Reynolds, and Marc A. Zissman, · 2003
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“Hierarchical text segmentation from multi-scale lexical cohesion,”
Jacob Eisenstein, · 2009
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“Formatting time-aligned ASR transcripts for readability,”
Maria Shugrina, · 2010
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“Adam: A method for stochastic optimization,”
Diederik P. Kingma and Jimmy Ba, · 2014
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“Unsupervised text segmentation using semantic relatedness graphs,”
Goran Glavaš, Federico Nanni, and Simone Paolo Ponzetto, · 2016
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Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean, · 2016
Cited alongside, same era.
“Text segmentation as a supervised learning task,”
Omri Koshorek, Adir Cohen, Noam Mor, Michael Rotman, and Jonathan Berant, · 2018
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“Segbot: A generic neural text segmentation model with pointer network,”
“Roberta: A robustly optimized BERT pretraining approach,”
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov, · 2019
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“Text segmentation by cross segment attention,”
Michal Lukasik, Boris Dadachev, Kishore Papineni, and Gonçalo Simões, · 2020
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“Two-level transformer and auxiliary coherence modeling for improved text segmentation,”
Goran Glavas and Swapna Somasundaran, · 2020
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“Structbert: Incorporating language structures into pre-training for deep language understanding,”
Wei Wang, Bin Bi, Ming Yan, Chen Wu, Jiangnan Xia, Zuyi Bao, Liwei Peng, and Luo Si, · 2020
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“Pretraining with contrastive sentence objectives improves discourse performance of language models,”
Dan Iter, Kelvin Guu, Larry Lansing, and Dan Jurafsky, · 2020
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Jing Li, Aixin Sun, and Shafiq R. Joty, · 2018
Cited alongside, same era.
“Attention-based neural text segmentation,”
Pinkesh Badjatiya, Litton J. Kurisinkel, Manish Gupta, and Vasudeva Varma, · 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
Cited alongside, same era.
“ELECTRA: pre-training text encoders as discriminators rather than generators,”
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning, · 2020
Later among the works it cites.