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Text-Summarization

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Abstractive and Extractive Deep Learning Methods for Text Summarisation

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# Text-Summarization
Abstractive and Extractive Deep Learning Methods for Text Summarization

# Single-Document Summarization
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).

2. Nallapati, Ramesh, Bing Xiang, and Bowen Zhou. "Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond". CoNLL (2016).

3. Sumit Chopra, Alexander M. Rush and Michael Auli. "Abstractive Sentence Summarization with Attentive Recurrent Neural Networks". NAACL (2016).

4. Jianpeng Cheng, Mirella Lapata. "Neural Summarization by Extracting Sentences and Words". ACL(2016)

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).

6. Abigail See, Peter J. Liu, Christopher D. Manning. "Get To The Point: Summarization with Pointer-Generator Networks". ACL (2017).

7. Qingyu Zhou, Nan Yang, Furu Wei, Ming Zhou. "Selective Encoding for Abstractive Sentence Summarization". ACL (2017)

8. Nallapati Ramesh, Bing Xiang, and Bowen Zhou. "SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documents."  AAAI(2017).

9. Jiatao Gu, Zhengdong Lu, Hang Li, Victor O.K. Li. "Incorporating Copying Mechanism in Sequence-to-Sequence Learning". ACL (2016).

10. Piji Li, Wai Lam, Lidong Bing, Zihao Wang. "Deep Recurrent Generative Decoder for Abstractive Text Summarization". EMNLP (2017).

11. Jin-ge Yao, Xiaojun Wan and Jianguo Xiao. "Recent Advances in Document Summarization". KAIS, survey paper, 2017.

12. Shibhansh Dohare, Harish Karnick. "Text Summarization using Abstract Meaning Representation". arxiv 2017

13. Jeffrey Ling, Alexander M. Rush."Coarse-to-Fine Attention Models for Document Summarization". NFiS@EMNLP 2017

14. Romain Paulus, Caiming Xiong, Richard Socher. "A Deep Reinforced Model for Abstractive Summarization". ICLR (2018).

15. Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, Yejin Choi. "Deep Communicating Agents for Abstractive Summarization". NAACL (2018).

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).

# Multi-Document Summarization
1. Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer. "Generating Wikipedia by Summarizing Long Sequences". ICLR (2018)

# Meeting Summarization
1. [Abstractive Meeting Summarization with Entailment and Fusion. ](https://pdfs.semanticscholar.org/3388/cf1186d5ca3ebf83dcad7530a0130075b6cf.pdf)

2. [A Template-based Abstractive Meeting Summarization: Leveraging Summary and Source Text Relationships. ](https://aclanthology.info/pdf/W/W14/W14-4407.pdf)

3. [Domain-Independent Abstract Generation for Focused Meeting Summarization.](https://www.cs.cornell.edu/home/cardie/papers/acl13-Domain.pdf)

4. [Generating and Validating Abstracts of Meeting Conversations: a User Study](https://www.aclweb.org/anthology/W/W10/W10-4211.pdf)

5. [Generating Abstractive Summaries from Meeting Transcripts](https://arxiv.org/abs/1609.07033)

# Basics
1. Ilya Sutskever, Oriol Vinyals, Quoc V. Le."Sequence to Sequence Learning with Neural Networks" . NIPS 2014.
2. 	Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio. "Neural Machine Translation by Jointly Learning to Align and Translate". CoRR (2014)
3. Thang Luong, Hieu Pham, Christopher D. Manning." Effective Approaches to Attention-based Neural Machine Translation". EMNLP 2015.

# Other References:
 - Tan, Jiwei, Xiaojun Wan, and Jianguo Xiao. "From Neural Sentence Summarization to Headline Generation: A Coarse-to-Fine Approach." IJCAI 2017
 - Piji Li, Wai Lam, Lidong Bing, Weiwei Guo, Hang Li. Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization EMNLP 2017
 - Jiwei Tan and Xiaojun Wan. "Abstractive Document Summarization with a Graph-Based Attentional Neural Model". ACL (2017).
 - "AttSum: Joint Learning of Focusing and Summarization with Neural Attention"
 - Abstractive Document Summarization via Neural Model with Joint Attention
- Attention Is All You Need
- Convolutional Sequence to Sequence Learning
- Wenyuan Zeng, Wenjie Luo, Sanja Fidler, Raquel Urtasun. "Efficient Summarization with Read-Again and Copy Mechanism." arXiv (2016)

- Gulcehre Caglar, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. "Pointing the Unknown Words". ACL (2016).
- Suzuki Jun, and Masaaki Nagata. "Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization". EACL (2017)

-  Linqing Liu, Yao Lu, Min Yang, Qiang Qu, Jia Zhu, Hongyan Li. "Generative Adversarial Network for Abstractive Text Summarization". To appear

- Xinyu Hua, Lu Wang. A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization NFiS@EMNLP 2017

- Towards Improving Abstractive Summarization via Entailment Generation
-  Paraphrase Generation with Deep Reinforcement Learning
- Angela Fan, David Grangier, Michael Auli  Controllable Abstractive Summarization

- Ziqiang Cao, Furu Wei, Wenjie Li, Sujian Li Faithful to the Original: Fact Aware Neural Abstractive Summarization

- Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset
Piji Li, Lidong Bing, Wai Lam

- 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

- TL;DR: Mining Reddit to Learn Automatic Summarization
- Paraphrase Generation with Deep Reinforcement Learning
- Exploring Teacher Forcing Techniques for Sequence-to-Sequence Abstractive Headline Summarization
- Generation for Articles using Abstractive Summarization
- Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision
- Dialogue Act Sequence Labeling using Hierarchical encoder with CRF
- Graph-Based Abstractive Summarization: Compression of Semantic Graphs
- Look-ahead Attention for Generation in Neural Machine Translation

- Neural Text Generation: A Practical Guide
- AHNN: An Attention-Based Hybrid Neural Network for Sentence Modeling
- A Sequential Neural Encoder With Latent Structured Description for Modeling Sentences
- Adversarial learning for neural dialogue generation. Santosh Kumar Bharti, Korra Sathya Babu 
- Detecting (Un)Important Content for Single-Document News Summarization
Yinfei Yang, Forrest Sheng Bao, Ani Nenkovay EACL 2017  
 - Towards Abstraction from Extraction: Multiple Timescale Gated Recurrent Unit for Summarization
Minsoo Kim, Moirangthem Dennis Singh, Minho Lee

- Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, and Noah A. Smith. Toward Abstractive Summarization Using Semantic Representations. NAACL 2015

- Graph-based Neural Multi-Document Summarization.
  
- Text summarization using unsupervised deep learning
 - Piji Li, Zihao Wang, Wai Lam, Zhaochun Ren, and Lidong Bing. 2017. Salience estimation via variational auto-encoders for multi-document summarization
 - Controlling output length in neural encoder-decoders
 - Abstractive Text Summarization Using Deep Learning

Compression
 - Language as a latent variable: Discrete generative models for sen- tence compression
 
Headline
 - Language as a Latent Variable: Discrete Generative Models for Sentence Compression
 - Neural headline generation with minimum risk training
 - Joint copying and restricted generation for paraphrase
 - Neural headline generation on abstract meaning representation
 - Source-side Prediction for Neural Headline Generation
 - Conceptual Multi-layer Neural Network Model for Headline Generation
 - Low-Resource Neural Headline Generation
 
 Extractive
 - Extractive Summarization: Limits, Compression, Generalized Model and Heuristics. Rakesh Verma, Daniel Lee 
 - Automatic Keyword Extraction for Text Summarization: A Survey
 - Extractive Summarization using Continuous Vector Space Models
 - Neural Extractive Summarization with Side Information

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