Summary of Studying and Recommending Information Highlighting in Stack Overflow Answers, by Shahla Shaan Ahmed et al.
Studying and Recommending Information Highlighting in Stack Overflow Answers
by Shahla Shaan Ahmed, Shaowei Wang, Yuan Tian, Tse-Hsun, Chen, Haoxiang Zhang
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a large-scale exploratory study on the information highlighted in Stack Overflow (SO) answers. It develops approaches to automatically recommend highlighted content with formatting styles using neural network architectures initially designed for Named Entity Recognition. The methods involve training CNN-based and BERT-based models for each type of formatting, such as Bold, Italic, Code, and Heading. The results show that the models achieve a precision ranging from 0.50 to 0.72 for different formatting types, with Code being easier to recommend. However, text formatting types (Heading, Bold, and Italic) suffer low recall due to missing identification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how people highlight important information on Stack Overflow. It uses computer programs called neural networks to help automatically suggest what parts of answers should be highlighted in bold, italics, code, or headings. The researchers looked at 31 million answers and found that the models were good at recommending code formatting but struggled with other types like heading or italic text. They think this might be because the models are better at recognizing common words than less common ones. |
Keywords
* Artificial intelligence * Bert * Cnn * Named entity recognition * Neural network * Precision * Recall
