Poster: Mujaz: A Summarization-based Approach for Normalized Vulnerability Description

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Presented at ACM CCS 2023 by

David Mohaisen

Hattan Althebeiti

Brett Fazio

William Chen

This work proposes a multi-task Natural Language Processing (NLP) system to normalize and summarize the descriptions into a uniform structure. A dataset was curated from an official public database and broken into several constituent entities representing a particular aspect of the description. A model is trained on the annotated features independently and jointly to generate a simple and uniform summary. We also introduce our human metrics to judge the quality of the generated summary with respect to human comprehension and content accuracy.