A. Bartoli and E. Medvet, Bibliometric Evaluation of Researchers in the Internet??Age, The Information Society, vol.6, issue.2, pp.349-354, 2014.
DOI : 10.1016/j.joi.2009.03.010

A. Csiszar, Peer review: Troubled from the start, Nature, vol.532, issue.7599, pp.306-308, 2016.
DOI : 10.1038/532306a
URL : http://www.nature.com:80/polopoly_fs/1.19763!/menu/main/topColumns/topLeftColumn/pdf/532306a.pdf

J. Beall, List of predatory publishers 2016. https://scholarlyoa.com/2016/01/ 05/bealls-list-of-predatory-publishers-2016 Accessed, pp.2016-2045

J. D. Bowman, Predatory Publishing, Questionable Peer Review, and Fraudulent Conferences, American Journal of Pharmaceutical Education, vol.78, issue.10, 2014.
DOI : 10.5688/ajpe7810176
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4315198/pdf

M. Dadkhah, A. M. Alharbi, M. H. Al-khresheh, T. Sutikno, T. Maliszewski et al., Affiliation oriented journals: Don't worry about peer review if you have good affiliation, International Journal of Electrical and Computer Engineering, vol.5, issue.4, p.621, 2015.

D. Butler, Investigating journals: The dark side of publishing, Nature, vol.495, issue.7442, pp.433-435, 2013.
DOI : 10.1038/495433a

N. Eldredge, Mathgen paper accepted! Technical report, Mathematics, 2012.

W. Oremus, This is what happens when no one proofreads an academic paper, 2014.

J. Qiu, M. Schrope, N. Jones, B. Borrell, J. Tollefson et al., Publish or perish in China, Nature, vol.463, issue.7278, pp.142-143, 2010.
DOI : 10.1038/463142a
URL : http://www.nature.com/news/2010/100112/pdf/463142a.pdf

T. Reller, Faking peer reviews, 2012.

J. Fischman, Fake peer reviews, the latest form of scientific fraud, fool journals, The Chronicle of Higher Education, 2012.

C. Ferguson, A. Marcus, and I. Oransky, Publishing: The peer-review scam, Nature, vol.515, issue.7528, pp.480-482, 2014.
DOI : 10.1038/515480a
URL : http://www.nature.com:80/polopoly_fs/1.16400!/menu/main/topColumns/topLeftColumn/pdf/515480a.pdf

E. Callaway, Faked peer reviews prompt 64 retractions, Nature, 2015.
DOI : 10.1038/nature.2015.18202

R. V. Noorden, Publishers withdraw more than 120 gibberish papers, Nature, 2014.
DOI : 10.1038/nature.2014.14763

T. H. Wen, M. Gasic, N. Mrk?i´mrk?i´c, P. H. Su, D. Vandyke et al., Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp.1711-1721, 2015.
DOI : 10.18653/v1/D15-1199
URL : http://arxiv.org/pdf/1508.01745

I. Sutskever, O. Vinyals, and Q. V. Le, Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp.3104-3112, 2014.

A. Wright, Algorithmic authors, Communications of the ACM, vol.58, issue.11, pp.12-14, 2015.
DOI : 10.1145/2820421

C. Labbé and D. Labbé, Duplicate and fake publications in the scientific literature: how many SCIgen papers in computer science?, Scientometrics, vol.50, issue.11, pp.379-396, 2013.
DOI : 10.1002/meet.14504301185

A. H. Oh and A. I. Rudnicky, Stochastic natural language generation for spoken dialog systems, Computer Speech & Language, vol.16, issue.3-4, pp.387-407, 2002.
DOI : 10.1016/S0885-2308(02)00012-8

A. Belz, Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models, Natural Language Engineering, vol.9, issue.04, pp.431-455, 2008.
DOI : 10.1007/s11168-006-6327-9

V. Rieser and O. Lemon, Natural language generation as planning under uncertainty for spoken dialogue systems In: Empirical methods in natural language generation, pp.105-120, 2010.

K. Kukich, Where do phrases come from: Some preliminary experiments in connectionist phrase generation. In: Natural language generation, pp.405-421, 1987.
DOI : 10.1007/978-94-009-3645-4_26

T. Mikolov, M. Karafiát, L. Burget, J. Cernock-`-cernock-`-y, and S. Khudanpur, Recurrent neural network based language model, In: INTERSPEECH, vol.2, issue.3, 2010.

S. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation, vol.4, issue.8, pp.1735-1780, 1997.
DOI : 10.1016/0893-6080(88)90007-X

A. Graves, Generating sequences with recurrent neural networks. arXiv preprint, 2013.

P. Potash, A. Romanov, and A. Rumshisky, GhostWriter: Using an LSTM for Automatic Rap Lyric Generation, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp.1919-1924, 2015.
DOI : 10.18653/v1/D15-1221
URL : http://aclweb.org/anthology/D/D15/D15-1221.pdf

X. Zhang and M. Lapata, Chinese Poetry Generation with Recurrent Neural Networks, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.670-680, 2014.
DOI : 10.3115/v1/D14-1074

A. Karpathy and L. Fei-fei, Deep visual-semantic alignments for generating image descriptions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3128-3137, 2015.
DOI : 10.1109/cvpr.2015.7298932
URL : http://arxiv.org/abs/1412.2306

J. Mao, W. Xu, Y. Yang, J. Wang, Z. Huang et al., Deep captioning with multimodal recurrent neural networks (m-rnn). arXiv preprint arXiv, pp.1412-6632, 2014.

O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, Show and tell: A neural image caption generator, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3156-3164, 2015.
DOI : 10.1109/CVPR.2015.7298935

S. Venugopalan, H. Xu, J. Donahue, M. Rohrbach, R. Mooney et al., Translating videos to natural language using deep recurrent neural networks. arXiv preprint arXiv, p.14124729, 2014.
DOI : 10.3115/v1/n15-1173
URL : http://arxiv.org/abs/1412.4729

K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv, pp.1405-3531, 2014.

J. R. Finkel, T. Grenager, and C. Manning, Incorporating non-local information into information extraction systems by Gibbs sampling, Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics , ACL '05, pp.363-370, 2005.
DOI : 10.3115/1219840.1219885
URL : http://acl.ldc.upenn.edu/p/p05/p05-1045.pdf

K. Toutanova, D. Klein, C. D. Manning, and Y. Singer, Feature-rich part-of-speech tagging with a cyclic dependency network, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology , NAACL '03, pp.173-180, 2003.
DOI : 10.3115/1073445.1073478

V. Narayanan, I. Arora, and A. Bhatia, Fast and Accurate Sentiment Classification Using an Enhanced Naive Bayes Model, Intelligent Data Engineering and Automated Learning?IDEAL 2013, pp.194-201, 2013.
DOI : 10.1007/978-3-642-41278-3_24

C. Manning and D. Klein, Optimization, maxent models, and conditional estimation without magic, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology Tutorials, NAACL '03, pp.8-8, 2003.
DOI : 10.3115/1075168.1075176