Finding duplicate documents by similarity refers to identifying files with nearly identical content despite having different names or minor text variations. This differs from simple name-based checks which only flag identical filenames, ignoring similar content across differently named documents. Advanced tools accomplish this by scanning text patterns, using techniques like fuzzy matching or hashing algorithms to detect near-replicates based on content similarity.

This approach is essential in contexts where multiple document versions exist. Legal teams use it to spot redundant contracts across large case files, avoiding inconsistent versions. Data analysts process customer feedback or survey responses, merging nearly identical entries like "very satisfied" and "quite satisfied" to accurately summarize sentiment without overcounting.

Similarity-based detection offers significant resource savings by eliminating redundant files, reducing storage and processing overhead. However, accuracy depends heavily on configuration: overly broad matching merges unrelated content, while too-strict settings miss legitimate duplicates. Ethical applications avoid bias during document consolidation. Advances in AI are enhancing nuance in similarity detection, particularly with complex documents like reports or code.