Abstractive summarization of long texts by representing multiple compositionalities with temporal hierarchical pointer generator network

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works

Sequence to Sequence Learning with Neural Networks

Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works

Neural Machine Translation by Jointly Learning to Align and Translate

Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213 · see citing works

Advances in Neural Information Processing Systems 29

Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works

Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008 · see citing works

Untitled work

(2004) · Cited by 9,718 · see citing works

Untitled work

(2010) · Cited by 8,261 · see citing works

On the Properties of Neural Machine Translation: Encoder–Decoder Approaches

Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014) · SSST@EMNLP · doi:10.3115/v1/w14-4012 · Cited by 7,251 · see citing works

Recurrent neural network based language model

Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390 · see citing works

Get To The Point: Summarization with Pointer-Generator Networks

See, A., Liu, P. J., & Manning, C. D. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1099 · Cited by 3,775 · see citing works

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317 · see citing works

The Automatic Creation of Literature Abstracts

Luhn, H. P. (1958) · IBM Journal of Research and Development · doi:10.1147/rd.22.0159 · Cited by 3,278 · see citing works

Long short-term memory recurrent neural network architectures for large scale acoustic modeling

Sak, H., Senior, A., & Beaufays, F. (2014) · Interspeech 2014 · doi:10.21437/interspeech.2014-80 · Cited by 2,973 · see citing works

Teaching Machines to Read and ComprehendPreprint

Hermann, K. M., Kočiský, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03340 · Cited by 2,779 · see citing works

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

(2016) · doi:10.18653/v1/d16-1 · Cited by 2,321 · see citing works

Automatic evaluation of summaries using N-gram co-occurrence statistics

Lin, C.-Y., & Hovy, E. H. (2003) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1073445.1073465 · Cited by 1,591 · see citing works

Gradient Flow in Recurrent Nets: The Difficulty of Learning LongTerm Dependencies

(2009) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470544037.ch14 · Cited by 1,312 · see citing works

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

(2022) · doi:10.18653/v1/2022.naacl-main · Cited by 1,116 · see citing works

On the Properties of Neural Machine Translation: Encoder-Decoder ApproachesPreprint

Cho, K., van Merriënboer, B., Bahdanau, D., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.1259 · Cited by 1,112 · see citing works

Deep neural networks: a new framework for modeling biological vision and brain information processing

Kriegeskorte, N. (2015) · Annual Review of Vision Science · doi:10.1146/annurev-vision-082114-035447 · Cited by 1,093 · see citing works

Cortical tracking of hierarchical linguistic structures in connected speech

Ding, N., Melloni, L., Zhang, H., Tian, X., & Poeppel, D. (2016) · Nature Neuroscience · doi:10.1038/nn.4186 · Cited by 1,059 · see citing works

Hierarchical modularity in human brain functional networks

David Meunier, A. (2009) · Frontiers in Neuroinformatics · doi:10.3389/neuro.11.037.2009 · Cited by 660 · see citing works

International 1989 Joint Conference on Neural Networks

(1989) · doi:10.1109/ijcnn.1989 · Cited by 609 · see citing works

A Hierarchical Neural Autoencoder for Paragraphs and Documents

Li, J., Luong, M.-T., & Jurafsky, D. (2015) · doi:10.3115/v1/p15-1107 · Cited by 507 · see citing works

Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment

Yamashita, Y., & Tani, J. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000220 · Cited by 468 · see citing works

Automatic Summarization

Nenkova, A. (2011) · now publishers, Inc. eBooks · doi:10.1561/9781601984715 · Cited by 457 · see citing works

Contributions to Legal Document Summarization: Judgments from the Portuguese Supreme Court of JusticePreprint

Dias, M. R., Ribeiro, R., & Pinto, H. S. (2019) · arXiv (Cornell University) · doi:10.4230/oasics.slate.2024.2 · Cited by 355 · see citing works

Automatic summarising: The state of the art

Jones, K. S. (2007) · Information Processing & Management · doi:10.1016/j.ipm.2007.03.009 · Cited by 354 · see citing works

The challenges of automatic summarization

Hahn, U., & Mani, I. (2000) · Computer · doi:10.1109/2.881692 · Cited by 352 · see citing works

Document Embedding with Paragraph VectorsPreprint

Dai, A. M., Olah, C., & Le, Q. V. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.07998 · Cited by 352 · see citing works

A Field Guide to Dynamical Recurrent Networks

Kolen, J. F., & Kremer, S. C. (2009) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470544037 · Cited by 338 · see citing works

LCSTS: A Large Scale Chinese Short Text Summarization Dataset

Hu, B., Chen, Q., & Zhu, F. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1229 · Cited by 325 · see citing works

Simple Recurrent Units for Highly Parallelizable Recurrence

Lei, T., Zhang, Y., Wang, S. I., Dai, H., & Artzi, Y. (2017) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1477 · Cited by 315 · see citing works

Text summarization using unsupervised deep learning

Yousefi-Azar, M., & Hamey, L. (2017) · Expert systems with applications · doi:10.1016/j.eswa.2016.10.017 · Cited by 232 · see citing works

A Hierarchical Neural Autoencoder for Paragraphs and DocumentsPreprint

Li, J., Luong, M.-T., & Jurafsky, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.01057 · Cited by 169 · see citing works

Evaluation challenges in large-scale document summarization

Radev, D., Teufel, S., Saggion, H., et al. (2003) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/1075096.1075144 · Cited by 154 · see citing works

Overcoming the Lack of Parallel Data in Sentence Compression

Filippova, K., & Altun, Y. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1155 · Cited by 132 · see citing works

Sequence-to-Sequence RNNs for Text SummarizationPreprint

Nallapati, R. M., Xiang, B., & Zhou, B. (2016) · arXiv (Cornell University) · Cited by 120 · see citing works

Multilevel structure in behaviour and in the brain: a model of Fuster's hierarchy

Botvinick, M. (2007) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2007.2056 · Cited by 110 · see citing works

A Neural Attention Model for Sentence Summarization

Rush, A. M., Chopra, S., & Weston, J. (2015) · Cited by 89 · see citing works

A topic modeling based approach to novel document automatic summarization

Wu, Z., Li, L., Li, G., et al. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.04.054 · Cited by 79 · see citing works

Extractive vs. NLG-based abstractive summarization of evaluative text

Carenini, G., & Cheung, J. C. K. (2008) · doi:10.3115/1708322.1708330 · Cited by 69 · see citing works

Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1

Hearst, M. A., & Ostendorf, M. (2003) · Cited by 68 · see citing works

Motor primitive and sequence self-organization in a hierarchical recurrent neural network

Paine, R. W., & Tani, J. (2004) · Neural Networks · doi:10.1016/j.neunet.2004.08.005 · Cited by 67 · see citing works

Artificial Neural Networks and Machine Learning – ICANN 2012

Bhattacharya, B. I. 0.-0.-5.-0. S., Coyle, D., Maguire, L., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33269-2 · Cited by 66 · see citing works

Sample Efficient Text Summarization Using a Single Pre-Trained TransformerPreprint

Khandelwal, U., Clark, K., Jurafsky, D., & Kaiser, Ł. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.08836 · Cited by 61 · see citing works

Towards Abstraction from Extraction: Multiple Timescale Gated Recurrent Unit for Summarization

Kim, M., Moirangthem, D. S., & Lee, M. (2016) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w16-1608 · Cited by 31 · see citing works

How to Improve Text Summarization and Classification by Mutual Cooperation on an Integrated Framework

Jeong, H., Ko, Y., & Seo, J. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.05.001 · Cited by 27 · see citing works

Diversifying customer review rankings

Krestel, R., & Dokoohaki, N. (2015) · Neural Networks · doi:10.1016/j.neunet.2015.02.008 · Cited by 21 · see citing works

Toward abstraction from multi-modal data: Empirical studies on multiple time-scale recurrent models

Zhong, J., Cangelosi, A., & Ogata, T. (2017) · doi:10.1109/ijcnn.2017.7966312 · Cited by 12 · see citing works

Adaptive Learning of Linguistic Hierarchy in a Multiple Timescale Recurrent Neural Network

Heinrich, S., Weber, C., & Wermter, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33269-2_70 · Cited by 9 · see citing works

Temporal hierarchies in multilayer gated recurrent neural networks for language models

Moirangthem, D. S., & Lee, M. (2017) · doi:10.1109/ijcnn.2017.7966115 · Cited by 9 · see citing works

Representing Compositionality based on Multiple Timescales Gated Recurrent Neural Networks with Adaptive Temporal Hierarchy for Character-Level Language Models

Moirangthem, D. S., Son, J., & Lee, M. (2017) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w17-2616 · Cited by 4 · see citing works

Proceedings of the Fifth International Natural Language Generation Conference on - INLG '08

(2008) · doi:10.3115/1708322 · Cited by 3 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805

Dissociating language and thought in large language models

Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.01.011 · Cited by 345 · see citing works

On the Applications of the Experience Summarization in Modern Teaching and Research

Xu, G. (2000) · Journal of Hubei Institute for Nationalities

Multilingual Summarization Evaluation without Human Models

Saggion, H., Torres‐Moreno, J., da Cunha, I., et al. (2010) · International Conference on Computational Linguistics · Cited by 76 · see citing works

Experiences with and Reflections on Text Summarization Tools

Liu, S. (2009) · International Journal of Computational Intelligence Systems · doi:10.1080/18756891.2009.9727654 · Cited by 10 · see citing works

Dynamic Summarization: Another Stride Towards Summarization

Zhang, J., Cheng, X., & Xu, H. (2007) · 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops · doi:10.1109/wiiatw.2007.4427541

Dynamic Summarization: Another Stride Towards Summarization

Zhang, J., Cheng, X., & Xu, H. (2007) · 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops · doi:10.1109/wi-iatw.2007.84