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Code summarization aims to generate concise natural language descriptions of source code, which can help improve program comprehension and maintenance.
Critical values and probability levels for the wilcoxon rank sum test and the wilcoxon signed rank test
Frank Wilcoxon, SK Katti, and Roberta A Wilcox. 1970 · 1970
Earlier work this paper cites.
The problem of learning long-term dependencies in recurrent networks
Yoshua Bengio, Paolo Frasconi, and Patrice Y. Simard. 1993 · 1993
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2016 · 2016
Earlier work this paper cites.
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Cited alongside, same era.
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Cited alongside, same era.
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Later among the works it cites.
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