Extract from Irfan Shuttari’s article “How Does Generative AI Auto Summarization Work and How Can it Be Applied to eDiscovery & Surveillance Workflows?”
It seems everyone is talking about generative AI and its capabilities these days. Many people in society have used – or at least tried – tools like ChatGPT and other generative AI models. There seems to be new stories about amazing capabilities of the models every day, and there also seems to be new stories about concerns about the technology – such as impact on jobs and the tendency for generative AI models to hallucinate – every day as well.
The excitement about the potential for generative AI has extended to the governance & compliance community, and providers have been rushing to quickly integrate generative AI capabilities and leverage the capabilities of large language models (LLMs) into their products. One of the capabilities that has received considerable attention is the ability for LLMs to apply auto summarization to document collections to enable legal & compliance professionals to quickly understand information within a document collection and use it to streamline decision making regarding specific documents. In this article, we’ll discuss how generative AI auto summarization works, how it can be applied to support eDiscovery & Surveillance workflows and the benefits and challenges of using auto summarization in eDiscovery.
What is Auto Summarization?
Auto summarization in the context of large language models involves generating concise summaries of longer texts. The aim is to capture the core ideas and essential information in a much shorter form. This process can be particularly valuable for digesting large volumes of text or understanding the key points of complex documents without needing to read through the entire content.
