# Summarizing Unstructured Logs in Online Services

arXiv: https://arxiv.org/abs/2012.08938 · HTML: https://mlsanity.com/paper/2012.08938/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges](https://mlsanity.com/paper/2304.04661/) (2023)
- [ChatGPT: A Study on its Utility for Ubiquitous Software Engineering Tasks](https://mlsanity.com/paper/2305.16837/) (2023)
- [An Assessment of ChatGPT on Log Data](https://mlsanity.com/paper/2309.07938/) (2023)
- [UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks](https://mlsanity.com/paper/2112.03159/) (2021)
- [R-Log: Incentivizing Log Analysis Capability in LLMs via Reasoning-based Reinforcement Learning](https://mlsanity.com/paper/2509.25987/) (2025)

## Abstract (arXiv)

Logs are one of the most valuable data sources for managing large-scale online services. After a failure is detected/diagnosed/predicted, operators still have to inspect the raw logs to gain a summarized view before take actions. However, manual or rule-based log summarization has become inefficient and ineffective. In this work, we propose LogSummary, an automatic, unsupervised end-to-end log summarization framework for online services. LogSummary obtains the summarized triples of important logs for a given log sequence. It integrates a novel information extraction method taking both semantic information and domain knowledge into consideration, with a new triple ranking approach using the global knowledge learned from all logs. Given the lack of a publicly-available gold standard for log summarization, we have manually labelled the summaries of four open-source log datasets and made them publicly available. The evaluation on these datasets as well as the case studies on real-world logs demonstrate that LogSummary produces a highly representative (average ROUGE F1 score of 0.741) summaries. We have packaged LogSummary into an open-source toolkit and hope that it can benefit for future NLP-powered summarization works.