2.1基本内容（功能）：
需要实现的功能：
1 抓取论文数据
2 根据论文数据的上下文训练lstm神经网络及编码器-译码器模型
3 获取一句用户输入为原始句子
4 将这个句子导入训练好的编码器-译码器模型，得到对应当前句子的句子
5 重复第4步，直到获取到足够的句子构成一段话。

2.2技术方案：
本文拟采用以下几个技术方案实现目标。

首先，为了提高准确度，即解决问题(1)，应采用深度学习及其相关方法作为基本框架。

3. 研究计划与安排

(1)2018/1/14—2018/2/22：确定选题，查阅文献，外文翻译和撰写开题报告；
(2)2018/2/23—2018/4/30：系统架构、程序设计与开发、系统测试与完善；
(3)2018/5/1—2018/5/25：撰写及修改毕业论文；
(4)2018/5/26—2018/6/6：准备答辩。

4. 参考文献（12篇以上）

1. Stribling, J., M. Krohn, and D. Aguayo, Scigen-an automatic cs paper generator. 2005.
2. Parveen, D. and M. Strube. Integrating Importance, Non-Redundancy and Coherence in Graph-Based Extractive Summarization. in IJCAI. 2015.
3. Erkan, G. and D.R. Radev, Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of Artificial Intelligence Research, 2004. 22: p. 457-479.
4. Liu, W., et al., Semantic summary automatic generation in news event. Concurrency and Computation: Practice and Experience, 2017. 29(24).
5. Zhang, C., et al. Text summarization based on sentence selection with semantic representation. in Tools with Artificial Intelligence (ICTAI), 2014 IEEE 26th International Conference on. 2014. IEEE.
6. Chen, J. and H. Zhuge, Automatic generation of related work through summarizing citations. Concurrency and Computation: Practice and Experience, 2017.
7. Wanner, L., S. Verlinde, and M. Alonso Ramos. Writing assistants and automatic lexical error correction: word combinatorics. in Electronic lexicography in the 21st century: thinking outside the paper. 2013. Trojina, Institute for Applied Slovene Studies/Eesti Keele Instituut.
8. Crossley, S., Z. Cai, and D.S. McNamara. Syntagmatic, paradigmatic, and automatic n-gram approaches to assessing essay quality. in Twenty-Fifth International FLAIRS Conference. 2012.
9. Liu, M., R.A. Calvo, and V. Rus, G-Asks: An intelligent automatic question generation system for academic writing support. Dialogue amp; Discourse, 2012. 3(2): p. 101-124.
10. Kannan, A., et al. Smart reply: Automated response suggestion for email. in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. ACM.
11. Ma, X. and E. Hovy, End-to-end sequence labeling via bi-directional lstm-cnns-crf. arXiv preprint arXiv:1603.01354, 2016.
12. Sutskever, I., O. Vinyals, and Q.V. Le. Sequence to sequence learning with neural networks. in Advances in neural information processing systems. 2014.
13. Cho, K., et al., Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014.
14. Wang, S. and J. Jiang, Machine comprehension using match-lstm and answer pointer. arXiv preprint arXiv:1608.07905, 2016.
15. Yang, Z., et al. Hierarchical attention networks for document classification. in Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.
16. Andor, D., et al., Globally normalized transition-based neural networks. arXiv preprint arXiv:1603.06042, 2016.