# Studying and Recommending Information Highlighting in Stack Overflow Answers

arXiv: https://arxiv.org/abs/2401.01472 · HTML: https://mlsanity.com/paper/2401.01472/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 57 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Large Language Models Struggle to Learn Long-Tail Knowledge](https://mlsanity.com/paper/2211.08411/) (2022)
- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [Portuguese Named Entity Recognition using BERT-CRF](https://mlsanity.com/paper/1909.10649/) (2019)
- [Memorization in NLP Fine-tuning Methods](https://mlsanity.com/paper/2205.12506/) (2022)
- [PTM4Tag: Sharpening Tag Recommendation of Stack Overflow Posts with Pre-trained Models](https://mlsanity.com/paper/2203.10965/) (2022)
- [An Empirical Study of Obsolete Answers on Stack Overflow](https://mlsanity.com/paper/1903.12282/) (2019)
- [Toxic Code Snippets on Stack Overflow](https://mlsanity.com/paper/1806.07659/) (2018)
- [Essential Sentences for Navigating Stack Overflow Answers](https://mlsanity.com/paper/1912.13455/) (2019)
- [DeepTagRec: A Content-cum-User based Tag Recommendation Framework for Stack Overflow](https://mlsanity.com/paper/1903.03941/) (2019)
- [Understanding the Impact of Text Highlighting in Crowdsourcing Tasks](https://mlsanity.com/paper/1909.02780/) (2019)
- [On-the-Fly Syntax Highlighting using Neural Networks](https://mlsanity.com/paper/2208.02815/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Framework for Evaluating API-oriented Code Generation in Large Language Models](https://mlsanity.com/paper/2409.15228/) (2024)
- [StackRAG Agent: Improving Developer Answers with Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.13840/) (2024)

## Abstract (arXiv)

Context: Navigating the knowledge of Stack Overflow (SO) remains challenging. To make the posts vivid to users, SO allows users to write and edit posts with Markdown or HTML so that users can leverage various formatting styles (e.g., bold, italic, and code) to highlight the important information. Nonetheless, there have been limited studies on the highlighted information. Objective: We carried out the first large-scale exploratory study on the information highlighted in SO answers in our recent study. To extend our previous study, we develop approaches to automatically recommend highlighted content with formatting styles using neural network architectures initially designed for the Named Entity Recognition task. Method: In this paper, we studied 31,169,429 answers of Stack Overflow. For training recommendation models, we choose CNN-based and BERT-based models for each type of formatting (i.e., Bold, Italic, Code, and Heading) using the information highlighting dataset we collected from SO answers. Results: Our models achieve a precision ranging from 0.50 to 0.72 for different formatting types. It is easier to build a model to recommend Code than other types. Models for text formatting types (i.e., Heading, Bold, and Italic) suffer low recall. Our analysis of failure cases indicates that the majority of the failure cases are due to missing identification. One explanation is that the models are easy to learn the frequent highlighted words while struggling to learn less frequent words (i.g., long-tail knowledge). Conclusion: Our findings suggest that it is possible to develop recommendation models for highlighting information for answers with different formatting styles on Stack Overflow.