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.
@article{arxiv.2012.08938,
title = {Summarizing Unstructured Logs in Online Services},
author = {Weibin Meng and Federico Zaiter and Yuheng Huang and Ying Liu and Shenglin Zhang and Yuzhe Zhang and Yichen Zhu and Tianke Zhang and En Wang and Zuomin Ren and Feng Wang and Shimin Tao and Dan Pei},
journal= {arXiv preprint arXiv:2012.08938},
year = {2020}
}