Near duplicate detection in eDiscovery (Premium)

Consider a set of documents to be reviewed in which a subset is based on the same template and has mostly the same boilerplate language, with a few differences here and there. If a reviewer could identify this subset, review one of them thoroughly, and review the differences for the rest, they would not have missed any unique information while taking only a fraction of time that would have taken them to read all documents cover to cover. Near duplicate detection groups textually similar documents together to help you make your review process more efficient.


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How does it work?

When near duplicate detection is run, the system parses every document with text. Then, it compares every document against each other to determine whether their similarity is greater than the set threshold. If it is, the documents are grouped together. Once all documents have been compared and grouped, a document from each group is marked as the "pivot"; in reviewing your documents, you can review a pivot first and review the other documents in the same near duplicate set, focusing on the difference between the pivot and the document that is in review.