Papers to be discussed in this session are: , .
 P. Fernandes, M. Allamanis, and M. Brockschmidt, “Structured neural summarization,” arXiv preprint arXiv:1811.01824, 2018.
 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.
 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.
 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.
 U. Alon, S. Brody, O. Levy, and E. Yahav, “Code2seq: Generating sequences from structured representations of code,” arXiv preprint arXiv:1808.01400, 2018.
 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.
 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.
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