LogSummary: Unstructured Log Summarization for Software Systems

Abstract

We propose LogSummary, an automatic, unsupervised end-to-end log summarization framework for software system maintenance in this work. LogSummary obtains the summarized triples of necessary logs for a given log sequence. It integrates a novel information extraction method that considers semantic information and domain knowledge 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 labeled the summaries of four open-source log datasets and made them publicly available. The evaluation of these datasets and the case studies on real-world logs demonstrate that LogSummary produces highly representative (average ROUGE F1 score of 0.741) summaries efficiently. We have packaged LogSummary into an open-source toolkit and hope it can be a standard baseline and benefit future log summarization works.