# LogPTR: Variable-Aware Log Parsing with Pointer Network

arXiv: https://arxiv.org/abs/2401.05986 · HTML: https://mlsanity.com/paper/2401.05986/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2024.

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

- [Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correction](https://mlsanity.com/paper/2406.03376/) (2024)
- [A Word is Worth 4-bit: Efficient Log Parsing with Binary Coded Decimal Recognition](https://mlsanity.com/paper/2506.01147/) (2025)

## Abstract (arXiv)

Due to the sheer size of software logs, developers rely on automated log analysis. Log parsing, which parses semi-structured logs into a structured format, is a prerequisite of automated log analysis. However, existing log parsers are unsatisfactory when applied in practice because they 1) ignore categories of variables, and 2) need labor-intensive model tuning. To address these limitations, we propose LogPTR, a variable-aware log parser that can extract the static and dynamic parts in logs, and further identify categories of variables. The key of LogPTR is formulating log parsing as a text summarization problem and using a pointer mechanism to copy words from the log message and label tokens indicating categories of variables. The experimental results on widely-used benchmark datasets show that LogPTR outperforms state-of-the-art log parsers on both general log parsing that extracts log templates and variable-aware log parsing that further identifies categories of variables.