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Code Summarization

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Greatest papers with code

code2seq: Generating Sequences from Structured Representations of Code

ICLR 2019 tech-srl/code2seq

The ability to generate natural language sequences from source code snippets has a variety of applications such as code summarization, documentation, and retrieval.


Automatic Source Code Summarization with Extended Tree-LSTM

19 Jun 2019sh1doy/summarization_tf

Neural machine translation models are used to automatically generate a document from given source code since this can be regarded as a machine translation task.


Improving Automatic Source Code Summarization via Deep Reinforcement Learning

17 Nov 2018mf1832146/tree-transformer

To the best of our knowledge, most state-of-the-art approaches follow an encoder-decoder framework which encodes the code into a hidden space and then decode it into natural language space, suffering from two major drawbacks: a) Their encoders only consider the sequential content of code, ignoring the tree structure which is also critical for the task of code summarization, b) Their decoders are typically trained to predict the next word by maximizing the likelihood of next ground-truth word with previous ground-truth word given.