Efficient Incident Summarization in ITOps: Leveraging Entity-Based Grouping
Bibliographic record
Abstract
An incident which is created due to a fault in an Application Monitoring System, gather large amount of diverse information, which helps in effective remediation of the fault. A Site Reliability Engineer (SRE) should resolve the outage quickly, for which all the fault related information should be presented to her in a crisp and summarized form. In this paper, we address this problem by summarizing an incident and presenting important details to the SRE. We group the list of related events, which is a part of the incident payload, and use Large Language Models (LLMs) to summarize each of these groups separately. The grouping is driven by the entities and symptoms occurring due to the fault, and used to design efficient prompts for LLM. Our approach addresses the known issue of LLM hallucination, and remove any false symptoms or facts from the generated summary. Our proposed method creates a resource-entity driven summarization, giving a bird's eye view of the entire outage in a cost and time efficient way, thus aiding an SRE to understand and resolve the incident at a faster pace.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".