What is generative AI auto summarization? And how can be applied in eDiscovery workflows? Irfan Shuttari of Veritas tells us here.
In his post (How Does Generative AI Auto Summarization Work and How Can it Be Applied to eDiscovery & Surveillance Workflows?, available here), Irfan discusses 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.
As Irfan notes, 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.
Sounds great, right? And there are several benefits to auto summarization. Here’s one of them:
Auto summarization is free of personal biases that may be present in humans who read and summarize documents.
However, while auto summarization sounds like a great feature, like any application of generative AI technology, it’s not always accurate, which could lead to misclassifications of documents. Here’s one of those challenges:
Accuracy and Completeness: Ensuring that the summary remains faithful to the original text can be challenging. There’s a risk of altering the intended meaning or omitting critical information, especially in large documents with multiple components.
So, what are the other benefits and challenges of generative AI auto summarization? How can it be applied to eDiscovery and electronic communication surveillance? And what are the underlying techniques in auto summarization? Find out here, it’s only one click! This is a manual summarization for which you need to read the entire article! 😉
So, what do you think? Have you applied generative AI based auto summarization in your eDiscovery workflows yet? Please share any comments you might have or if you’d like to know more about a particular topic.
Image created using Bing Image Creator Powered by DALL-E, using the term “robot looking at one long document and one short document on a computer screen”.
Disclosure: Veritas is an Educational Partner and sponsor of eDiscovery Today
Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by my employer, my partners or my clients. eDiscovery Today is made available solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscovery Today should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.
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Doug-
I am trying to wrap my head around the value of AI automatically summarizing in the litigation context. As a trial lawyer, I wanted my summaries to reduce documents, depositions, and interviews to relevant information focused on the evidence and theme of the case. Otherwise, you just end up with more items to look through with clutter and distractions. We only summarized key documents and depos and focused on the issues we needed to prove or disprove. I think I would rather use the cost of my AI (or human assistance) to search documents for relevant evidence than to summarize it and risk missing what I really need or providing useless extra information.
I must admit that I took my own depositions and did my own fact research, summaries, and trial prep, except for expert help summarizing medical records. But the idea of auto summaries “free of personal biases” (unfocused?) does not seem like something that would suit my needs for litigation. Am I missing something?
Judge Artigliere,
The biggest potential benefit as I understand it is in doc review for responsiveness. Sometimes, documents to be reviewed can be very long and a doc reviewer has to go through the entire document to determine whether or not it’s responsive to discovery requests. With a document summary, they MAY be able to much more quickly make that determination without having to review the entire document.
Having said that, the summaries aren’t always as accurate or as comprehensive as they could be, especially with large documents involving multiple topics. As is the case with any review, QC and statistical sampling of the results is recommended, including whether summaries accurately reflect the content of the documents (and how often they don’t). It’s a new technological approach, of course, so there aren’t any case studies yet (that I know of) which discuss how effective it is.
As for cost, it varies widely. Sometimes, it’s baked into the platform; other times, there are token charges for those summaries. It’s important to know what you’re paying for.