, Catastrophic forgetting of the RL decoder on the Gigaword dev set, p.71

, Evolution on test set during training

. .. , Repetition comparisons by length (lower is better), p.73

. .. Length, L. Amplayo, and . Hwang, 74 xxii List of Tables 1.1 An example on abstractive summarization taken verbatim from, 2018.

, The icons are chosen from Wikipedia's list

. .. , 34 3.3 Accuracy of our proposed models and of state-of-the-art models from the litterature

. .. The-english-gigaword, Data statistics for, p.63

. .. , Baseline (Seq2Seq trained on Sentence/Compression Pairs) vs. RL Select-and-Paraphrase Model (trained on S-Tree Data), p.64

,. Auxl, . 1l:ar, and .. .. Al:1r, Oracle Results, The last row S-tree+ includes Stree, 1L:1R

, Example of oracle and full source generation

. .. , Performance comparisons between models, p.71

.. .. Human-evaluations,

. .. Rennie, 75 FIGURE 5.14: Self-critical sequence training (SCST), 2017.

Q. Ain and . Tul, Sentiment Analysis Using Deep Learning Techniques: A Review, 2017.

M. Allahyari, Text Summarization Techniques: A Brief Survey, 2017.

R. Amplayo, S. Kim, S. Lim, and . Hwang, Entity Commonsense Representation for Neural Abstractive Summarization, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol.1, pp.697-707, 2018.

J. Austin and . Langshaw, How to do things with words, William James Lectures, pp.5-6, 1962.

D. Bahdanau, K. Cho, and Y. Bengio, Neural Machine Translation by Jointly Learning to Align and Translate, 2014.

D. Bahdanau, An Actor-Critic Algorithm for Sequence Prediction, 2016.

S. Banerjee and A. Lavie, METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments, Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, pp.65-72, 2005.

M. Banko, Open Information Extraction from the Web, Proceedings of the 20th International Joint Conference on Artifical Intelligence. IJCAI'07, pp.2670-2676, 2007.

S. Bengio, Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks, Proceedings of the 28th International Conference on Neural Information Processing Systems, vol.1, pp.1171-1179, 2015.

Y. Bengio, R. Ducharme, and P. Vincent, A Neural Probabilistic Language Model, pp.932-938, 2000.

D. Bespalov, Sentiment classification based on supervised latent n-gram analysis, Proceedings of the 11th International Workshop on Semantic Evaluation, pp.375-382, 2011.

L. Bing, Abstractive Multi-Document Summarization via Phrase Selection and Merging, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol.1, pp.1587-1597, 2015.

J. Bollen, H. Mao, and X. Zeng, Twitter mood predicts the stock market, Journal of Computational Science, vol.2, issue.1, pp.1877-7503, 2011.

B. E. Boser, M. Isabelle, V. N. Guyon, and . Vapnik, A Training Algorithm for Optimal Margin Classifiers, Proceedings of the Fifth Annual Workshop on Computational Learning Theory. COLT '92, pp.144-152, 1992.

K. Boyer, An Affect-Enriched Dialogue Act Classification Model for Task-Oriented Dialogue, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp.1190-1199, 2011.

S. Bridle and . John, Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition, pp.227-236, 1990.

A. Z. Broder, Syntactic Clustering of the Web, Selected Papers from the Sixth International Conference on World Wide Web, pp.1157-1166, 1997.

H. Bunt, Dialogue Act Annotation with the ISO 24617-2 Standard, 2017.

Z. Cao, Faithful to the Original: Fact Aware Neural Abstractive Summarization, 2017.

Z. Cao, Retrieve, rerank and rewrite: Soft template based neural summarization, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol.1, pp.152-161, 2018.

A. Celikyilmaz, Deep Communicating Agents for Abstractive Summarization, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.1662-1675, 2018.

C. Cerisara, Multi-task dialog act and sentiment recognition on Mastodon, Proceedings of the 27th International Conference on Computational Linguistics, pp.745-754, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01838323

Y. Chen and M. Bansal, Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol.1, pp.675-686, 2018.

T. Chiang, Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer, ArXiv abs/1903.08953. Chomsky, Noam, 1957.

J. Chung, Empirical evaluation of gated recurrent neural networks on sequence modeling, NIPS 2014 Workshop on Deep Learning, 2014.

C. Clavel and Z. Callejas, Sentiment Analysis: From Opinion Mining to Human-Agent Interaction, IEEE Trans. Affect. Comput, issue.1, pp.1949-3045, 2016.

A. Cohan, A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol.2, pp.615-621, 2018.

T. Cohn and M. Lapata, Sentence Compression Beyond Word Deletion, Proceedings of the 22nd International Conference on Computational Linguistics, pp.137-144, 2008.

, Sentence Compression As Tree Transduction, J. Artif. Int. Res, vol.34, issue.1, pp.1076-9757, 2009.

R. Collobert and J. Weston, A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning, Proceedings of the 25th International Conference on Machine Learning. ICML '08, pp.160-167, 2008.

R. Collobert, Natural Language Processing (Almost) from Scratch". In: J. Mach. Learn. Res, vol.12, pp.1532-4435, 2011.

A. Conneau, Very Deep Convolutional Networks for Natural Language Processing, 2016.

M. G. Core and J. F. Allen, Coding Dialogs with the DAMSL Annotation Scheme, Working Notes of the AAAI Fall Symposium on Communicative Action in Humans and Machines, pp.28-35, 1997.

N. Crook, R. Granell, and S. G. Pulman, Unsupervised Classification of Dialogue Acts using a Dirichlet Process Mixture Model, SIGDIAL Conference, 2009.

D. Iii, J. Hal, D. Langford, and . Marcu, Search-based Structured Prediction, Mach. Learn, vol.75, issue.3, pp.297-325, 2009.

K. Dave, S. Lawrence, and D. M. Pennock, Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews, Proceedings of the 12th International Conference on World Wide Web. WWW '03, pp.519-528, 2003.

J. Deriu, SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision, 2016.

J. Devlin, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol.1, pp.4171-4186, 2019.

B. Dhingra, Gated-Attention Readers for Text Comprehension, In: ACL (1). Ed. by Regina Barzilay and Min-Yen Kan. Association for Computational Linguistics, pp.1832-1846, 2017.

P. Dodds and . Sheridan, Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter, 2011.

. Fan and . Rong-en, LIBLINEAR: A Library for Large Linear Classification, J. Mach. Learn. Res, vol.9, pp.1532-4435, 2008.

K. Filippova and Y. Altun, Overcoming the Lack of Parallel Data in Sentence Compression, Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp.1481-1491, 2013.

K. Filippova and M. Strube, Sentence Fusion via Dependency Graph Compression, Proceedings of the Conference on Empirical Methods in Natural Language Processing. EMNLP '08, pp.177-185, 2008.

K. Filippova, Sentence Compression by Deletion with LSTMs, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp.360-368, 2015.

E. N. Forsythand, H. Craig, and . Martell, Lexical and Discourse Analysis of Online Chat Dialog, International Conference on Semantic Computing (ICSC 2007), pp.19-26, 2007.

M. Gamon, Pulse: Mining Customer Opinions from Free Text, Proceedings of the 6th International Conference on Advances in Intelligent Data Analysis. IDA'05, pp.121-132, 2005.

A. Gatt and E. Reiter, SimpleNLG: A Realisation Engine for Practical Applications, Proceedings of the 12th European Workshop on Natural Language Generation, pp.90-93, 2009.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proceedings of Machine Learning Research 9, pp.249-256, 2010.

A. Go, R. Bhayani, and L. Huang, Twitter Sentiment Classification using Distant Supervision, Processing, pp.1-6, 2009.

Y. Goldberg and G. Hirst, Neural Network Methods in Natural Language Processing, 2017.

T. Hasegawa, Predicting and Eliciting Addressee's Emotion in Online Dialogue, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, vol.1, pp.964-972, 2013.

K. He, Deep Residual Learning for Image Recognition, 2016.

J. Herzig, Classifying Emotions in Customer Support Dialogues in Social Media, SIGDIAL Conference, 2016.

G. Hinton, Deep Neural Networks for Acoustic Modeling in Speech Recognition, Signal Processing Magazine, 2012.

S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural computation 9, vol.8, pp.1735-1780, 1997.

M. Hu and B. Liu, Mining and Summarizing Customer Reviews, Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD '04, pp.168-177, 2004.

G. Huang, Densely Connected Convolutional Networks, 2016.

S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, pp.448-456, 2015.

M. Jeong, C. Lin, and G. Lee, Semi-supervised Speech Act Recognition in Emails and Forums, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol.3, pp.1250-1259, 2009.

T. Joachims, Text categorization with Support Vector Machines: Learning with many relevant features, Machine Learning: ECML-98. Ed. by Claire Nédellec and Céline Rouveirol, pp.978-981, 1998.

A. Joulin, Bag of Tricks for Efficient Text Classification, 2016.

N. Kalchbrenner, E. Grefenstette, and P. Blunsom, A Convolutional Neural Network for Modelling Sentences, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol.1, pp.655-665, 2014.

M. Kaya, G. Fidan, and I. Toroslu, Transfer Learning Using Twitter Data for Improving Sentiment Classification of Turkish Political News, In: ISCIS. Ed. by Erol Gelenbe and Ricardo Lent, vol.264, pp.139-148, 2013.

M. Kim and H. Kim, Integrated neural network model for identifying speech acts, predicators, and sentiments of dialogue utterances, Pattern Recognition Letters, vol.101, pp.1-5, 2018.

Y. Kim, Convolutional Neural Networks for Sentence Classification, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1746-1751, 2014.

Y. Kim, Character-aware Neural Language Models, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI'16, pp.2741-2749, 2016.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, 2014.

E. Kiperwasser and M. Ballesteros, Scheduled Multi-Task Learning: From Syntax to Translation, Transactions of the Association for Computational Linguistics, vol.6, pp.225-240, 2018.

P. Koehn and R. Knowles, Six Challenges for Neural Machine Translation, Proceedings of the First Workshop on Neural Machine Translation. Vancouver: Association for Computational Linguistics, pp.28-39, 2017.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012.

W. Kryscinski, Improving Abstraction in Text Summarization, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp.1808-1817, 2018.

J. D. Lafferty, A. Mccallum, and F. C. Pereira, Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, Proceedings of the Eighteenth International Conference on Machine Learning. ICML '01, pp.282-289, 2001.

A. Le and . Nguyen, Improving Sequence to Sequence Neural Machine Translation by Utilizing Syntactic Dependency Information, 2017.

H. T. Le, C. Cerisara, and A. Denis, Do Convolutional Networks Need to Be Deep for Text Classification ?, In: The Workshops of the The Thirty-Second AAAI Conference on Artificial Intelligence, pp.29-36, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01690601

H. T. Le, C. Cerisara, and C. Gardent, Quality of syntactic implication of RL-based sentence summarization, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02883327

H. T. Le, C. Cerisara, and C. Gardent, RL Extraction of Syntax-Based Chunks for Sentence Compression, Artificial Neural Networks and Machine Learning -ICANN 2019: Text and Time Series -28th International Conference on Artificial Neural Networks, pp.337-347, 2019.

Y. Lecun, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86, vol.11, pp.18-9219, 1998.

G. Letarte, Importance of Self-Attention for Sentiment Analysis, Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. Brussels, Belgium: Association for Computational Linguistics, pp.267-275, 2018.

C. Li, Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol.2, pp.55-60, 2018.

H. Li, Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization, Proceedings of the 27th International Conference on Computational Linguistics, pp.1430-1441, 2018.

J. Li, Modeling Source Syntax for Neural Machine Translation, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol.1, pp.688-697, 2017.

J. Li, K. Jessy, A. Thadani, and . Stent, The Role of Discourse Units in Near-Extractive Summarization, Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Los Angeles, pp.137-147, 2016.

C. Lin, ROUGE: A Package for Automatic Evaluation of Summaries, Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, pp.74-81, 2004.

H. Lin and V. Ng, Abstractive Summarization: A Survey of the State of the Art, Proceedings of the AAAI Conference on Artificial Intelligence, vol.33, pp.9815-9822, 2019.

B. Liu, Sentiment Analysis and Opinion Mining, p.1608458849, 2012.

H. P. Luhn, The Automatic Creation of Literature Abstracts, In: IBM J. Res. Dev, vol.2, issue.2, pp.18-8646, 1958.

A. L. Maas, Learning Word Vectors for Sentiment Analysis, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol.1, pp.978-979, 2011.

A. Mccallum and K. Nigam, A Comparison of Event Models for Naive Bayes Text Classification, Learning for Text Categorization: Papers from the 1998 AAAI Workshop, pp.41-48, 1998.

R. Mcdonald, Structured Models for Fine-to-Coarse Sentiment Analysis, Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp.432-439, 2007.

R. Mihalcea and P. Tarau, TextRank: Bringing Order into Text, Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp.404-411, 2004.

T. Mikolov, Distributed Representations of Words and Phrases and their Compositionality, Advances in Neural Information Processing Systems 26, pp.3111-3119, 2013.

Y. Miyamoto and K. Cho, Gated Word-Character Recurrent Language Model, 2016.

J. Moreno, . Iste, and . Wiley, Automatic text summarization, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02562444

P. Nakov, SemEval-2013 Task 2: Sentiment Analysis in Twitter, Proceedings of the Seventh International Workshop on Semantic Evaluation, vol.2, pp.312-320, 2013.

P. Nakov, Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts, vol.50, pp.35-65, 2016.

P. Nakov, Semeval-2016 task 4: Sentiment analysis in twitter, Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp.1-18, 2016.

R. Nallapati, F. Zhai, and B. Zhou, Summarunner: A recurrent neural network based sequence model for extractive summarization of documents, Thirty-First AAAI Conference on Artificial Intelligence, 2017.

R. Nallapati, Abstractive Text Summarization using Sequenceto-sequence RNNs and Beyond, Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning, pp.280-290, 2016.

. Narayan, S. B. Shashi, M. Cohen, and . Lapata, Ranking Sentences for Extractive Summarization with Reinforcement Learning, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol.1, pp.1747-1759, 2018.

A. Nenkova and K. R. Mckeown, Automatic Summarization, Foundations and Trends in Information Retrieval, vol.5, issue.2-3, pp.103-233, 2011.

A. Nenkova and R. Passonneau, Evaluating Content Selection in Summarization: The Pyramid Method, Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL, pp.145-152, 2004.

M. Norouzi, Reward Augmented Maximum Likelihood for Neural Structured Prediction, Advances in Neural Information Processing Systems 29, pp.1723-1731, 2016.

N. Novielli and C. Strapparava, The Role of Affect Analysis in Dialogue Act Identification, IEEE Transactions on Affective, pp.439-451, 2013.

R. Pasunuru and M. Bansal, Multi-Reward Reinforced Summarization with Saliency and Entailment, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.646-653, 2018.

R. Paulus, C. Xiong, and R. Socher, A deep reinforced model for abstractive summarization, Proceedings of the 6th International Conference on Learning Representations, 2018.

A. Pluwak, Towards Application of Speech Act Theory to Opinion Mining, pp.33-44, 2016.

M. I. Prabha and G. U. Srikanth, Survey of Sentiment Analysis Using Deep Learning Techniques, 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), pp.1-9, 2019.

Q. Qian, Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol.1, pp.1365-1374, 2015.

M. Qureshi, . Atif, O. Colm, G. 'riordan, and . Pasi, Clustering with Error-Estimation for Monitoring Reputation of Companies on Twitter, Lecture Notes in Computer Science, vol.8281, 2013.

A. Radford, R. Jozefowicz, and I. Sutskever, Learning to Generate Reviews and Discovering Sentiment, 2017.

A. Radford, Language Models are Unsupervised Multitask Learners, 2019.

M. &. Ranzato and A. , Sequence Level Training with Recurrent Neural Networks, 4th International Conference on Learning Representations, 2016.

Y. Ren, Improving Twitter Sentiment Classification Using Topic-Enriched Multi-Prototype Word Embeddings, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp.3038-3044, 2016.

S. J. Rennie, Self-Critical Sequence Training for Image Captioning, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1179-1195, 2017.

A. Ritter, C. Cherry, and W. B. Dolan, Unsupervised Modeling of Twitter Conversations, 2010.

F. Rosenblatt, The Perceptron: A Probabilistic Model for Information Storage and Organization in The Brain, Psychological Review, pp.65-386, 1958.

S. Rosenthal, N. Farra, and P. Nakov, SemEval-2017 Task 4: Sentiment Analysis in Twitter, Proceedings of the 11th International Workshop on Semantic Evaluation, pp.502-518, 2017.

S. Rosenthal, Proceedings of the 8th International Workshop on Semantic Evaluation, vol.9, pp.73-80, 2014.

A. M. Rush, J. Sumit-chopra, and . Weston, A Neural Attention Model for Abstractive Sentence Summarization, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp.379-389, 2015.

B. Sankaran, Temporal Attention Model for Neural Machine Translation, 2016.

C. Santos, M. Nogueira, and . Gatti, Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts, pp.69-78, 2014.

R. Schank and R. Abelson, Scripts, plans, goals and understanding: An inquiry into human knowledge structures, 1977.
URL : https://hal.archives-ouvertes.fr/hal-00692030

B. Schölkopf, Estimating the Support of a High-Dimensional Distribution, Neural Comput. 13.7, pp.899-7667, 2001.

H. Schwenk, Very Deep Convolutional Networks for Text Classification, In: EACL (1), pp.1107-1116, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01454940

A. See, P. J. Liu, and C. D. Manning, Get To The Point: Summarization with Pointer-Generator Networks, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol.1, 2017.

C. Vancouver,

R. Sennrich and B. Haddow, Linguistic Input Features Improve Neural Machine Translation, Proceedings of the First Conference on Machine Translation, pp.83-91, 2016.

R. Sennrich, B. Haddow, and A. Birch, Neural Machine Translation of Rare Words with Subword Units, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol.1, 2016.

G. Berlin, , pp.1715-1725

J. Shawe-taylor and N. Cristianini, Kernel Methods for Pattern Analysis, p.521813972, 2004.

R. Socher, Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions, EMNLP. ACL, pp.151-161, 2011.

R. Socher, Semantic Compositionality through Recursive Matrix-Vector Spaces, pp.1201-1211, 2012.

R. Socher, Recursive deep models for semantic compositionality over a sentiment treebank, Proceedings of the conference on empirical methods in natural language processing (EMNLP), vol.1631, p.1642, 2013.

K. Song, L. Zhao, and F. Liu, Structure-Infused Copy Mechanisms for Abstractive Summarization, CoRR abs/1806.05658, 2018.

A. Sordoni, A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion, 2015.

A. Stolcke, Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech, Comput. Linguist, vol.26, issue.3, pp.891-2017, 2000.

I. Sutskever, O. Vinyals, Q. Le, and ;. Z. Ghahramani, Sequence to Sequence Learning with Neural Networks, Advances in Neural Information Processing Systems 27, pp.3104-3112, 2014.

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 1998.

H. Tanaka, Syntax-Driven Sentence Revision for Broadcast News Summarization, Proceedings of the 2009 Workshop on Language Generation and Summarisation, pp.39-47, 2009.

D. Tang, B. Qin, and T. Liu, Deep Learning for Sentiment Analysis: Successful Approaches and Future Challenges, Int. Rev. Data Min. and Knowl. Disc. 5, vol.6, pp.292-303, 2015.

D. Tang, Coooolll: A Deep Learning System for Twitter Sentiment Classification, 2014.

D. Tang, Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification, In: ACL (1), pp.1555-1565, 2014.

M. Thelwall, K. Buckley, and G. Paltoglou, Sentiment strength detection for the social web, Journal of the American Society for Information Science and Technology, 2012.

M. Thelwall, P. Sud, and F. Vis, Commenting on YouTube videos: From guatemalan rock to El Big Bang, pp.616-629, 2012.

Z. Tu, Modeling Coverage for Neural Machine Translation, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol.1, pp.76-85, 2016.

A. M. Turing, Computing Machinery and Intelligence, English. In: Mind. New Series, vol.59, pp.433-460, 1950.

A. Vaswani, Attention is All you Need, Advances in Neural Information Processing Systems, vol.30, pp.5998-6008, 2017.

A. Venkatraman, J. A. Hebert, and . Bagnell, Improving Multistep Prediction of Learned Time Series Models, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI'15, 2015.

O. Vinyals, M. Fortunato, and N. Jaitly, Pointer Networks, Proceedings of the 28th International Conference on Neural Information Processing Systems, vol.2, pp.2692-2700, 2015.

O. Vinyals, Target-Dependent Twitter Sentiment Classification with Rich Automatic Features, 2015.

S. Vosoughi and D. Roy, Tweet Acts: A Speech Act Classifier for Twitter, 2016.

X. Wang, W. Jiang, and Z. Luo, Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts, pp.2428-2437, 2016.

R. J. Williams, Simple statistical gradient-following algorithms for connectionist reinforcement learning, Machine Learning, pp.229-256, 1992.

T. Winograd, Procedures as a Representation for Data in a Computer Program for Understanding Natural Language, 1971.

K. Woodsend and M. Lapata, Learning to Simplify Sentences with Quasi-Synchronous Grammar and Integer Programming, Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp.409-420, 2011.

M. Woszczyna and A. H. Waibel, Inferring linguistic structure in spoken language, 1994.

Y. Wu, Google's neural machine translation system: Bridging the gap between human and machine translation, 2016.

Y. Xiao and K. Cho, Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers, 2016.

J. Xu, Discourse-Aware Neural Extractive Model for Text Summarization, 2019.

W. Xu, Optimizing Statistical Machine Translation for Text Simplification, Transactions of the Association for Computational Linguistics, vol.4, pp.401-415, 2016.

Z. Yang, R. Salakhutdinov, and W. W. Cohen, Multi-Task Cross-Lingual Sequence Tagging from Scratch, 2016.

D. Yogatama, Generative and Discriminative Text Classification with Recurrent Neural Networks, 2017.

J. Yosinski, How transferable are features in deep neural networks?, In: Advances in Neural Information Processing Systems 27, pp.3320-3328, 2014.

W. Zaremba and I. Sutskever, Reinforcement Learning Neural Turing Machines, 2015.

E. Zarisheva and T. Scheffler, Dialog Act Annotation for Twitter Conversations, SIGDIAL Conference, 2015.

L. Zhang, S. Johnny, B. Wang, and . Liu, Deep Learning for Sentiment Analysis : A Survey, Interdiscip. Rev. Data Min. Knowl. Discov, vol.8, 2018.

X. Zhang, J. Zhao, and Y. Lecun, The present paper has considerably more experimental results and a rewritten introduction, Text Understanding from Scratch" was posted in Feb 2015 as, vol.28, 2015.

X. Zhang and M. Lapata, Sentence Simplification with Deep Reinforcement Learning, Proceedings of EMNLP, 2017.

Y. Zhang and B. Wallace, A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification, 2015.