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1610 Journal of Software 软件学报 Vol.32, No.6, June 2021

[22] Liu Y, Lapata M. Text summarization with pretrained encoders. In: Proc. of the 2019 Conf. on Empirical Methods in Natural
Language Processing and the 9th Int’l Joint Conf. on Natural Language Processing (EMNLP-IJCNLP). Hong Kong: Association for
Computational Linguistics, 2019. 3730−3740. [doi: 10.18653/v1/D19-1387]
[23] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. In: Proc. of the 3rd Int’l Conf.
on Learning Representations (ICLR 2015). 2015.
[24] See A, Liu PJ, Manning CD. Get to the point: Summarization with pointer-generator networks. In: Proc. of the Annual Meeting of
the Association for Computational Linguistics (Vol.1: Long Papers). Vancouver: Association for Computational Linguistics, 2017.
1073−1083. [doi: 10.18653/v1/P17-1099]
[25] Gehrmann S, Deng YT, Rush AM. Bottom-Up abstractive summarization. In: Proc. of the 2018 Conf. on Empirical Methods in
Natural Language Processing. Brussels: Association for Computational Linguistics, 2018. 4098−4109.
[26] Liu F, Flanigan J, Thomson S, Sadeh N, Smith NA. Toward abstractive summarization using semantic representations. In: Proc. of
the 2015 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
Denver: Association for Computational Linguistics, 2015. 1077−1086.
[27] Yu LT, Zhang WN, Yu Y. Seqgan: Sequence generative adversarial nets with policy gradient. In Proc. of the 31st AAAI Conf. on
Artificial Intelligence (AAAI 2017). San Francisco: AAAI, 2017. 2852−2858.
[28] Fumio N, Nakano YI., Takase Y. Predicting meeting extracts in group discussions using multimodal convolutional neural networks.
In: Proc. of the 19th ACM Int’l Conf. on Multimodal Interaction. New York: Association for Computing Machinery, 2017.
421−425. [doi: https://doi.org/10.1145/3136755.3136803]
[29] Pan H, Zhou JP, Zhao Z, Liu Y, Cai D, Yang M. Dial2desc: End-to-end dialogue description generation. arXiv preprint arXiv:1811.
00185, 2018.
[30] Liu CY, Wang P, Xu J, Li Z, Ye JP. Automatic dialogue summary generation for customer service. In: Proc. of the 25th ACM
SIGKDD Int’l Conf. on Knowledge Discovery & Data Mining. New York: Association for Computing Machinery, 2019.
1957−1965. [doi: https://doi.org/10.1145/3292500.3330683]
[31] Tao X, Zhang XX, Guo SL, Zhang LM. Automatic summarization of user-generated content in academic Q&A community based
on Word2Vec and MMR. Data Analysis and Knowledge Discovery, 2020,4(4):109−118 (in Chinese with English abstract).
[32] Kyunghyun C, Merriënboer BV, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations
using RNN encoder-decoder for statistical machine translation. In: Proc. of the 2014 Conf. on Empirical Methods in Natural
Language Processing (EMNLP). Doha: Association for Computational Linguistics, 2014. 1724−1734.
[33] Jiang SY, Armaly A, McMillan C. Automatically generating commit messages from diffs using neural machine translation. In: Proc.
of the 2017 32nd IEEE/ACM Int’l Conf. on Automated Software Engineering (ASE). Urbana: IEEE, 2017. 135−146.
[34] Xu SB, Yao Y, Xu F, Gu TX, Tong HH, Lu J. Commit message generation for source code changes. In: Proc. of the 28th Int’l Joint
Conf. on Artificial Intelligence (IJCAI). 2019. 3975−3981.
[35] Hu X, Li G, Xia X, Lo D, Jin Z. Deep code comment generation. In: Proc. of the 2018 IEEE/ACM 26th Int’l Conf. on Program
Comprehension (ICPC). Gothenburg: IEEE, 2018. 200−210.
[36] Hu X, Li G, Xia X, Lo D, Jin Z. Deep code comment generation with hybrid lexical and syntactical information. Empirical
Software Engineering, 2020,25:2179−2217. [doi: https://doi.org/10.1007/s10664-019-09730-9]
[37] Alon U, Zilberstein M, Levy O, Yahav E. code2vec: Learning distributed representations of code. Proc. of the ACM on
Programming Languages, 2019,3:1−29. [doi: https://doi.org/10.1145/3290353]
[38] Ye DH, Xing ZC, Foo CY, Ang ZQ, Li J, Kapre N. Software-Specific named entity recognition in software engineering social
content. In: Proc. of the 2016 IEEE 23rd Int’l Conf. on Software Analysis, Evolution, and Reengineering (SANER). Suita: IEEE,
2016. 90−101. [doi: 10.1109/SANER.2016.10]
[39] Markovtsev V, Long W, Bulychev E, Keramitas R, Slavnov K, Markowski G. Splitting source code identifiers using bidirectional
LSTM recurrent neural network. arXiv preprint arXiv:1805.11651, 2018.
[40] Ferrari A., Esuli A. An NLP approach for cross-domain ambiguity detection in requirements engineering. Automated Software
Engineering, 2019,26:559−598. [doi: https://doi.org/10.1007/s10515-019-00261-7]
[41] Chen H, Damevski K, Shepherd D, Kraft NA. Modeling hierarchical usage context for software exceptions based on interaction
data. Automated Software Engineering, 2019,26:733−756. [doi: https://doi.org/10.1007/s10515-019-00265-3]
[42] Alreshedy K, Dharmaretnam D, German DM, Srinivasan V, Gulliver TA. SCC++: Predicting the programming language of
questions and snippets of StackOverflow. Journal of Systems and Software, 2020,162:110505. [doi: 10.1016/j.jss.2019.110505]