What are steps involved in Log Summarisation

Log summarization using ML models typically involves using natural language processing (NLP) techniques to analyze and summarize log data. Here's a general approach to performing log summarization using ML models:

Data Preparation:

Collect the log data that you want to summarize. Log data can be in various formats such as text files, CSV files, or database entries.

Preprocess the log data by cleaning and formatting it. This may involve removing irrelevant information, normalizing text, removing punctuation, and handling special characters or symbols.

Data Annotation:

Annotate the log data by manually creating summaries for a subset of log entries. This step involves reading each log entry and writing a concise summary that captures the key information.

Dataset Creation:

Split the annotated data into a training set and a validation/test set. The training set will be used to train the ML model, while the validation/test set will be used to evaluate the model's performance.

Feature Extraction:

Convert the log data into numerical or vector representations that ML models can understand. Common techniques include tokenization, vectorization (e.g., using TF-IDF or word embeddings), and feature engineering.

Model Training:

Select an appropriate ML model for log summarization, such as sequence-to-sequence models, transformer models, or recurrent neural networks (RNNs).

Train the ML model using the annotated log data. This typically involves feeding the log data and corresponding summaries as input-output pairs to the model and optimizing its parameters.

Model Evaluation:

Evaluate the trained model's performance on the validation/test set. Common evaluation metrics for text summarization include ROUGE scores, which measure the quality of the generated summaries compared to the reference summaries.

Model Deployment:

Once the ML model performs well on the validation/test set, you can deploy it for log summarization tasks. This may involve integrating the model into your existing log processing pipeline or creating a dedicated API or service for log summarization.

Continuous Improvement:

Monitor and evaluate the model's performance in production. Collect feedback from users and use it to iteratively improve the model's accuracy and usefulness.

It's important to note that log summarization is a complex task, and the specific implementation details and choice of ML model may vary depending on your specific requirements and the characteristics of your log data. It's recommended to explore existing research and libraries related to text summarization and adapt them to your specific use case.