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Code comment generation is the task of generating a high-level naturallanguage description for a given code method or function. Although researchershave been studying multiple ways to generate code comments automatically,previous work mainly considers representing a code token in its entiretysemantics form only (e.g., a language model is used to learn the semantics of acode token), and additional code properties such as the tree structure of acode are included as an auxiliary input to the model. There are twolimitations: 1) Learning the code token in its entirety form may not be able tocapture information succinctly in source code, and 2) The code token does notcontain additional syntactic information, inherently important in programminglanguages.  In this paper, we present LAnguage Model and Named Entity Recognition(LAMNER), a code comment generator capable of encoding code constructseffectively and capturing the structural property of a code token. Acharacter-level language model is used to learn the semantic representation toencode a code token. For the structural property of a token, a Named EntityRecognition model is trained to learn the different types of code tokens. Theserepresentations are then fed into an encoder-decoder architecture to generatecode comments. We evaluate the generated comments from LAMNER and otherbaselines on a popular Java dataset with four commonly used metrics. Ourresults show that LAMNER is effective and improves over the best baseline modelin BLEU-1, BLEU-2, BLEU-3, BLEU-4, ROUGE-L, METEOR, and CIDEr by 14.34%,18.98%, 21.55%, 23.00%, 10.52%, 1.44%, and 25.86%, respectively. Additionally,we fused LAMNER's code representation with the baseline models, and the fusedmodels consistently showed improvement over the non-fused models. The humanevaluation further shows that LAMNER produces high-quality code comments.

LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition