└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Text-Summarization 2 | Abstractive and Extractive Deep Learning Methods for Text Summarization 3 | 4 | 5 | # Single-Document Summarization 6 | 1. Alexander M. Rush, Sumit Chopra, Jason Weston. [A Neural Attention Model for Abstractive Sentence Summarization](http://aclweb.org/anthology/D/D15/D15-1044.pdf). EMNLP (2015). 7 | 8 | 2. Nallapati, Ramesh, Bing Xiang, and Bowen Zhou. "Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond". CoNLL (2016). 9 | 10 | 3. Sumit Chopra, Alexander M. Rush and Michael Auli. "Abstractive Sentence Summarization with Attentive Recurrent Neural Networks". NAACL (2016). 11 | 12 | 4. Jianpeng Cheng, Mirella Lapata. "Neural Summarization by Extracting Sentences and Words". ACL(2016) 13 | 14 | 5. Kristina Toutanova, Chris Brockett, Ke M. Tran, Saleema Amershi. "A Dataset and Evaluation Metrics for Abstractive Compression of Sentences and Short Paragraphs". EMNLP (2016). 15 | 16 | 6. Abigail See, Peter J. Liu, Christopher D. Manning. "Get To The Point: Summarization with Pointer-Generator Networks". ACL (2017). 17 | 18 | 7. Qingyu Zhou, Nan Yang, Furu Wei, Ming Zhou. "Selective Encoding for Abstractive Sentence Summarization". ACL (2017) 19 | 20 | 8. Nallapati Ramesh, Bing Xiang, and Bowen Zhou. "SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documents." AAAI(2017). 21 | 22 | 9. Jiatao Gu, Zhengdong Lu, Hang Li, Victor O.K. Li. "Incorporating Copying Mechanism in Sequence-to-Sequence Learning". ACL (2016). 23 | 24 | 10. Piji Li, Wai Lam, Lidong Bing, Zihao Wang. "Deep Recurrent Generative Decoder for Abstractive Text Summarization". EMNLP (2017). 25 | 26 | 11. Jin-ge Yao, Xiaojun Wan and Jianguo Xiao. "Recent Advances in Document Summarization". KAIS, survey paper, 2017. 27 | 28 | 12. Shibhansh Dohare, Harish Karnick. "Text Summarization using Abstract Meaning Representation". arxiv 2017 29 | 30 | 13. Jeffrey Ling, Alexander M. Rush."Coarse-to-Fine Attention Models for Document Summarization". NFiS@EMNLP 2017 31 | 32 | 14. Romain Paulus, Caiming Xiong, Richard Socher. "A Deep Reinforced Model for Abstractive Summarization". ICLR (2018). 33 | 34 | 15. Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, Yejin Choi. "Deep Communicating Agents for Abstractive Summarization". NAACL (2018). 35 | 36 | 16. Arman Cohan,Franck Dernoncourt,Doo Soon Kim,Trung Bui,Seokhwan Kim, Walter Chang, Nazli Goharian. "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents". NAACL (2018). 37 | 38 | # Multi-Document Summarization 39 | 1. Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer. "Generating Wikipedia by Summarizing Long Sequences". ICLR (2018) 40 | 41 | 42 | # Meeting Summarization 43 | 1. [Abstractive Meeting Summarization with Entailment and Fusion. ](https://pdfs.semanticscholar.org/3388/cf1186d5ca3ebf83dcad7530a0130075b6cf.pdf) 44 | 45 | 2. [A Template-based Abstractive Meeting Summarization: Leveraging Summary and Source Text Relationships. ](https://aclanthology.info/pdf/W/W14/W14-4407.pdf) 46 | 47 | 3. [Domain-Independent Abstract Generation for Focused Meeting Summarization.](https://www.cs.cornell.edu/home/cardie/papers/acl13-Domain.pdf) 48 | 49 | 4. [Generating and Validating Abstracts of Meeting Conversations: a User Study](https://www.aclweb.org/anthology/W/W10/W10-4211.pdf) 50 | 51 | 5. [Generating Abstractive Summaries from Meeting Transcripts](https://arxiv.org/abs/1609.07033) 52 | 53 | 54 | # Basics 55 | 1. Ilya Sutskever, Oriol Vinyals, Quoc V. Le."Sequence to Sequence Learning with Neural Networks" . NIPS 2014. 56 | 2. Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio. "Neural Machine Translation by Jointly Learning to Align and Translate". CoRR (2014) 57 | 3. Thang Luong, Hieu Pham, Christopher D. Manning." Effective Approaches to Attention-based Neural Machine Translation". EMNLP 2015. 58 | 59 | # Other References: 60 | - Tan, Jiwei, Xiaojun Wan, and Jianguo Xiao. "From Neural Sentence Summarization to Headline Generation: A Coarse-to-Fine Approach." IJCAI 2017 61 | - Piji Li, Wai Lam, Lidong Bing, Weiwei Guo, Hang Li. Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization EMNLP 2017 62 | - Jiwei Tan and Xiaojun Wan. "Abstractive Document Summarization with a Graph-Based Attentional Neural Model". ACL (2017). 63 | - "AttSum: Joint Learning of Focusing and Summarization with Neural Attention" 64 | - Abstractive Document Summarization via Neural Model with Joint Attention 65 | - Attention Is All You Need 66 | - Convolutional Sequence to Sequence Learning 67 | - Wenyuan Zeng, Wenjie Luo, Sanja Fidler, Raquel Urtasun. "Efficient Summarization with Read-Again and Copy Mechanism." arXiv (2016) 68 | 69 | - Gulcehre Caglar, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. "Pointing the Unknown Words". ACL (2016). 70 | - Suzuki Jun, and Masaaki Nagata. "Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization". EACL (2017) 71 | 72 | - Linqing Liu, Yao Lu, Min Yang, Qiang Qu, Jia Zhu, Hongyan Li. "Generative Adversarial Network for Abstractive Text Summarization". To appear 73 | 74 | - Xinyu Hua, Lu Wang. A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization NFiS@EMNLP 2017 75 | 76 | - Towards Improving Abstractive Summarization via Entailment Generation 77 | - Paraphrase Generation with Deep Reinforcement Learning 78 | - Angela Fan, David Grangier, Michael Auli Controllable Abstractive Summarization 79 | 80 | - Ziqiang Cao, Furu Wei, Wenjie Li, Sujian Li Faithful to the Original: Fact Aware Neural Abstractive Summarization 81 | 82 | - Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset 83 | Piji Li, Lidong Bing, Wai Lam 84 | 85 | - Shuming Ma, Xu Sun, Jingjing Xu, Houfeng Wang, Wenjie Li, Qi Su Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization 86 | 87 | - TL;DR: Mining Reddit to Learn Automatic Summarization 88 | - Paraphrase Generation with Deep Reinforcement Learning 89 | - Exploring Teacher Forcing Techniques for Sequence-to-Sequence Abstractive Headline Summarization 90 | - Generation for Articles using Abstractive Summarization 91 | - Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision 92 | - Dialogue Act Sequence Labeling using Hierarchical encoder with CRF 93 | - Graph-Based Abstractive Summarization: Compression of Semantic Graphs 94 | - Look-ahead Attention for Generation in Neural Machine Translation 95 | 96 | - Neural Text Generation: A Practical Guide 97 | - AHNN: An Attention-Based Hybrid Neural Network for Sentence Modeling 98 | - A Sequential Neural Encoder With Latent Structured Description for Modeling Sentences 99 | - Adversarial learning for neural dialogue generation. Santosh Kumar Bharti, Korra Sathya Babu 100 | - Detecting (Un)Important Content for Single-Document News Summarization 101 | Yinfei Yang, Forrest Sheng Bao, Ani Nenkovay EACL 2017 102 | - Towards Abstraction from Extraction: Multiple Timescale Gated Recurrent Unit for Summarization 103 | Minsoo Kim, Moirangthem Dennis Singh, Minho Lee 104 | 105 | - Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, and Noah A. Smith. Toward Abstractive Summarization Using Semantic Representations. NAACL 2015 106 | 107 | - Graph-based Neural Multi-Document Summarization. 108 | 109 | - Text summarization using unsupervised deep learning 110 | - Piji Li, Zihao Wang, Wai Lam, Zhaochun Ren, and Lidong Bing. 2017. Salience estimation via variational auto-encoders for multi-document summarization 111 | - Controlling output length in neural encoder-decoders 112 | - Abstractive Text Summarization Using Deep Learning 113 | 114 | Compression 115 | - Language as a latent variable: Discrete generative models for sen- tence compression 116 | 117 | Headline 118 | - Language as a Latent Variable: Discrete Generative Models for Sentence Compression 119 | - Neural headline generation with minimum risk training 120 | - Joint copying and restricted generation for paraphrase 121 | - Neural headline generation on abstract meaning representation 122 | - Source-side Prediction for Neural Headline Generation 123 | - Conceptual Multi-layer Neural Network Model for Headline Generation 124 | - Low-Resource Neural Headline Generation 125 | 126 | Extractive 127 | - Extractive Summarization: Limits, Compression, Generalized Model and Heuristics. Rakesh Verma, Daniel Lee 128 | - Automatic Keyword Extraction for Text Summarization: A Survey 129 | - Extractive Summarization using Continuous Vector Space Models 130 | - Neural Extractive Summarization with Side Information 131 | 132 | 133 | --------------------------------------------------------------------------------