The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Text summarization using unsupervised deep learning
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Citations
Cites
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
An algorithm for suffix stripping
Porter, M. F. (1980) · Program · doi:10.1108/eb046814 · Cited by 8,096 · cited 1×
“Stemming using Porter (1980) and stop-word removal 1 reduces this to 46,603 words comprising 7478 unique words.”
MethodTraining Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 3×
“Carreira-Perpinan and Hinton (2005) numerically compared the update rule with exact log-likelihood gradient with CD-k (Hinton, 2002).”
BackgroundResultHansen, L. K., & Salamon, P. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.58871 · Cited by 4,260
Learning to rank using gradient descent
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102363 · Cited by 2,836
Practical Recommendations for Gradient-Based Training of Deep Architectures
Bengio, Y. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_26 · Cited by 2,259
Neural Networks: Tricks of the Trade: Second Edition
(2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8 · Cited by 2,116
Cited by
Explainable artificial intelligence: a comprehensive review
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10088-y · Cited by 983
Moirangthem, D. S., & Lee, M. (2020) · Neural Networks · doi:10.1016/j.neunet.2019.12.022 · Cited by 30
Metadata
- Authors
- Yousefi-Azar, M., & Hamey, L.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 68
- Pages
- 93-105
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2530979053
- Cited by
- 247
- References
- 9
- FWCI
- 17.98
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Text summarization using unsupervised deep learning. https://app.cochat.ai/papers/10.1016/j.eswa.2016.10.017
- The paper
- Yousefi-Azar, M., & Hamey, L. (2017). Text summarization using unsupervised deep learning. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2016.10.017
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
