Text Deduplication Tool

Text deduplication is the process of identifying and removing near-duplicate or highly similar sentences or paragraphs from text. This tool uses the Levenshtein distance algorithm to calculate similarity between text segments and removes those that exceed your specified similarity threshold.

The similarity threshold is a percentage (50-100%) that determines how similar two text segments must be to be considered duplicates. 100% means exact match only, while lower values (like 80%) allow for minor differences. Start with 80% for most use cases, increase for stricter matching, or decrease to catch more variations.

Yes, the tool supports two modes: sentence-level deduplication (splits text by periods, exclamation marks, and question marks) and paragraph-level deduplication (splits by line breaks). Choose the mode that best fits your content structure and cleaning needs.

Fuzzy matching identifies text segments that are similar but not identical. This is useful for detecting duplicates that have minor variations like different punctuation, small typos, or slightly different wording. It's more powerful than exact matching for real-world content cleaning.

Case-insensitive matching (default) treats "Hello" and "hello" as the same, which is usually preferred for content deduplication. Use case-sensitive matching if capitalization differences are meaningful in your content, such as when dealing with proper nouns or technical terms.

By default, the tool keeps the first occurrence and removes subsequent duplicates. You can uncheck "Keep first occurrence" to keep the last occurrence instead. This is useful when later versions of text might be more refined or correct.

The tool identifies all groups of duplicates separately. If text segment A matches B, and C matches D, they form two distinct groups. The analysis shows you how many duplicate groups were found and their similarity percentages, giving you full insight into the deduplication process.

Common use cases include: cleaning up articles with repetitive sentences, merging documents that have overlapping content, removing duplicate paragraphs from web scraping results, consolidating similar feedback or survey responses, and improving content quality by eliminating redundancy.

Yes, the tool can process documents of various sizes. However, performance may vary with extremely large texts (over 10,000 sentences or paragraphs) due to the computational complexity of fuzzy matching. For best performance, consider processing very large documents in smaller sections.

The Levenshtein distance algorithm is highly accurate for detecting text similarity based on character-level differences. It calculates the minimum number of single-character edits needed to change one string into another. The similarity percentage gives you precise control over what counts as a duplicate.

The tool preserves the content of deduplicated text segments but reformats them based on the selected mode. Sentence mode joins deduplicated sentences with periods and spaces, while paragraph mode separates them with double line breaks. Original formatting like bold, italics, or special characters is maintained within each segment.

The analysis shows: original count of sentences/paragraphs, count after deduplication, number of duplicates removed, reduction percentage, and details of each duplicate group including similarity scores. This helps you understand the extent of duplication in your content and verify the results.