A document summarizer via Natural Language Processing
Description
The AI system utilizes state-of-the-art transformer-based models (e.g., BART, Pegasus, T5, SciBERT) to perform text summarization. These models are deployed entirely within the agency’'s secure firewalls, ensuring no external data exposure and no external access. BART, Pegasus, T5 and SciBERT are state-of-the-art, open-source AI models designed for Natural Language Processing (NLP) tasks. The model will be able to summarize lengthy internal documents, helping employees quickly extract key insights from a large volume of information. This tool automates summarization, reducing the time employees spend reading lengthy documents, enhancing productivity by accurately identifying key points in such documents. The traditional extractive summarization (e.g., via Term Frequency-Inverse Document Frequency TF-IDF) selects key sentences verbatim from the source, often misses several key elements of the document, and results in an inaccurate representation of the content. Unlike traditional extractive summarization, generative summarization (used by BART, Pegasus and T5) creates modified sentences that better capture the meaning and context of the document. This leads to more natural, coherent and concise summaries which enhance readability and contextual accuracy.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Controls / human review
ATO: Not reported; PIA: Not published