Papers to be discussed in this session are: [1], [2].

## Bibliography

[1] P. Fernandes, M. Allamanis, and M. Brockschmidt, “Structured neural summarization,” arXiv preprint arXiv:1811.01824, 2018.

[2] S. Iyer, I. Konstas, A. Cheung, and L. Zettlemoyer, “Summarizing source code using a neural attention model,” in Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: Long papers), 2016, pp. 2073–2083.

[3] M. Allamanis, H. Peng, and C. Sutton, “A convolutional attention network for extreme summarization of source code,” in International conference on machine learning, 2016, pp. 2091–2100.

[4] J. Fowkes, P. Chanthirasegaran, R. Ranca, M. Allamanis, M. Lapata, and C. Sutton, “Autofolding for source code summarization,” IEEE Trans. Softw. Eng., vol. 43, no. 12, pp. 1095–1109, Dec. 2017.

[5] U. Alon, S. Brody, O. Levy, and E. Yahav, “Code2seq: Generating sequences from structured representations of code,” arXiv preprint arXiv:1808.01400, 2018.

[6] Y. Wan et al., “Improving automatic source code summarization via deep reinforcement learning,” in Proceedings of the 33rd acm/ieee international conference on automated software engineering, 2018, pp. 397–407.

[7] S. Xu, S. Zhang, W. Wang, X. Cao, C. Guo, and J. Xu, “Method name suggestion with hierarchical attention networks,” in Proceedings of the 2019 acm sigplan workshop on partial evaluation and program manipulation, 2019, pp. 10–21.