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Comment Generation

Article commenting poses new challenges for machines, as it involves multiple cognitive abilities: understanding the given article, formulating opinions and arguments, and organizing natu ral language for expression.

Papers

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Showing 26–50 of 65 papers

TitleStatusHype
Automating Horizon Scanning in Future Studies—0
CoNT: Contrastive Neural Text GenerationCode2
InCoder: A Generative Model for Code Infilling and SynthesisCode2
LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition—0
Automating Code Review Activities by Large-Scale Pre-trainingCode1
Topic Analysis for Text with Side Data—0
Energy-bounded Learning for Robust Models of Code—0
Exploring Methods for Generating Feedback Comments for Writing LearningCode0
Impact of Evaluation Methodologies on Code SummarizationCode1
Shared Task on Feedback Comment Generation for Language Learners—0
Towards Controlled and Diverse Generation of Article Comments—0
Learning to Generate Code Comments from Class Hierarchies—0
API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations—0
Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection—0
InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees—0
Learning to Represent Programs with Heterogeneous Graphs—0
Market Comment Generation from Data with Noisy Alignments—0
Learning with Contrastive Examples for Data-to-Text GenerationCode0
One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music CommentCode0
Towards Context-Aware Code Comment Generation—0
Retrieve and Refine: Exemplar-based Neural Comment GenerationCode1
MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection ModelsCode0
Generating Pertinent and Diversified Comments with Topic-aware Pointer-Generator Networks—0
TAG : Type Auxiliary Guiding for Code Comment Generation—0
Creating Corpora for Research in Feedback Comment Generation—0
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