Icpc 2020 Improved Code Summarization

Icpc 2020 Improved Code Summarization Information Guide

Introduction to Icpc 2020 Improved Code Summarization
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Introduction to Icpc 2020 Improved Code Summarization

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Code summaries are brief natural language descriptions of source code pieces. The main purpose of [ICPC 26] EyeLayer: Integrating Human Attention Patterns into LLM-Based Code Summarization Authors: Sakib Haque, Alexander LeClair, Lingfei Wu, Collin McMillan Track: Technical Session: ML4SE (30 Jun

Important Facts

Famous ICPC 2020 - A Human Study of Comprehension and Code Summarization Net Worth

Explore the primary sources for Icpc 2020 Improved Code Summarization.

History

GypSum: Learning Hybrid Representations for Code Summarization Net Worth

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Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach

[ICPC 26] EyeLayer: Integrating Human Attention Patterns into LLM-Based Code Summarization

Semantic Similarity Metrics for Evaluating Source Code Summarization

MIT is first to solve problem C

Project-Level Encoding for Neural Source Code Summarization of Subroutines

M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source Code Summarization

A Multi-Modal Transformer-based Code Summarization Approach for Smart Contracts

ICPC 2020 - Full conference streaming

Improved Automatic Summarization of Subroutines via Attention to File Context

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Last Updated: June 17, 2026

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Famous ICPC 2020 - Improving Code Search with Co-Attentive Representation Learning Net Worth

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ICPC 2020 - Improved Code Summarization via a Graph Neural Network

ICPC 2020 - Improved Code Summarization via a Graph Neural Network

ICPC 2020

ICPC 2020 - A Human Study of Comprehension and Code Summarization

ICPC 2020 - A Human Study of Comprehension and Code Summarization

ICPC 2020

GypSum: Learning Hybrid Representations for Code Summarization

GypSum: Learning Hybrid Representations for Code Summarization

Presented at

ICPC 2020 - Improving Code Search with Co-Attentive Representation Learning

ICPC 2020 - Improving Code Search with Co-Attentive Representation Learning

ICPC 2020

Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach

Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach

Code summaries are brief natural language descriptions of source code pieces. The main purpose of

[ICPC 26] EyeLayer: Integrating Human Attention Patterns into LLM-Based Code Summarization

[ICPC 26] EyeLayer: Integrating Human Attention Patterns into LLM-Based Code Summarization

[ICPC 26] EyeLayer: Integrating Human Attention Patterns into LLM-Based Code Summarization

Semantic Similarity Metrics for Evaluating Source Code Summarization

Semantic Similarity Metrics for Evaluating Source Code Summarization

Presented at

MIT is first to solve problem C

MIT is first to solve problem C

MIT is first to solve problem C

Project-Level Encoding for Neural Source Code Summarization of Subroutines

Project-Level Encoding for Neural Source Code Summarization of Subroutines

Source

M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source Code Summarization

M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source Code Summarization

Presented at

A Multi-Modal Transformer-based Code Summarization Approach for Smart Contracts

A Multi-Modal Transformer-based Code Summarization Approach for Smart Contracts

Code

ICPC 2020 - Full conference streaming

ICPC 2020 - Full conference streaming

Inter.

Improved Automatic Summarization of Subroutines via Attention to File Context

Improved Automatic Summarization of Subroutines via Attention to File Context

Authors: Sakib Haque, Alexander LeClair, Lingfei Wu, Collin McMillan Track: Technical Session: ML4SE (30 Jun