Unique Sentence Extractor & Deduplicator — Why it helps

When scraping content, collecting user feedback, or processing transcripts, duplicate and near-duplicate sentences are common. They inflate dataset size, skew frequency statistics, and reduce quality of training data. Extracting unique sentences and grouping near-duplicates simplifies downstream workflows: it speeds manual review, reduces storage, and improves model training by avoiding repeated examples.

This tool extracts sentence-like units, normalizes them, detects exact duplicates and groups similar sentences using a fast shingle-based similarity (Jaccard). Tune the similarity threshold, ignore numbers or stopwords during comparison, and export results as CSV. Everything runs locally in your browser — ideal for private or sensitive text.

How it works (high level)

1. Split text into sentences using punctuation and line breaks. 2. Normalize (lowercase unless preserving case, trim, optional number removal). 3. Convert each sentence into token shingles and compute Jaccard similarity. 4. Cluster sentences with similarity ≥ threshold. The most frequent sentence in a cluster becomes the representative.

Practical tips

• Start with high threshold (85–90%) for near-identical text; lower (60–75%) for paraphrase grouping.
• Enable "Ignore numbers" to avoid grouping variance caused by IDs/dates.
• Use sort-by-frequency to review the most repeated content first.

Limitations

Shingle-Jaccard is fast and practical but not perfect; it may miss deep semantic paraphrases. For higher accuracy, consider embedding-based similarity (server-side). Use this tool for quick preprocessing and follow with manual review.