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Identifying document similarity using a fast estimation of the Levenshtein Distance based on compression and signatures

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Peter Coates, Frank Breitinger

Information Retrieval · Submitted 21 Jul 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our evaluation shows promising results in terms of runtime efficiency and accuracy.

how it is stated · asserted 0.86 read the record →

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arXiv ID2307.11496 (version 1)
Pith IDNS3DM3W2, Pith's permanent record of this paper and its review
CitationFrank Breitinger, Peter Coates. "Identifying document similarity using a fast estimation of the Levenshtein Distance based on compression and signatures." arXiv:2307.11496 (2023). https://pith.science/paper/NS3DM3W2
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,494 input tokens.

What the abstract itself states, as probabilities: claims first 0.24 · states limitation 0.53 · human participants 0.04 · controlled comparison 0.06 · randomized assignment 0.01 · states quantitative result 0.36 · compares with prior results 0.22 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Identifying document similarity has many applications, e.g., source code analysis or plagiarism detection. However, identifying similarities is not trivial and can be time complex. For instance, the Levenshtein Distance is a common metric to define the similarity between two documents but has quadratic runtime which makes it impractical for large documents where large starts with a few hundred kilobytes. In this paper, we present a novel concept that allows estimating the Levenshtein Distance: the algorithm first compresses documents to signatures (similar to hash values) using a user-defined compression ratio. Signatures can then be compared against each other (some constrains apply) where the outcome is the estimated Levenshtein Distance. Our evaluation shows promising results in terms of runtime efficiency and accuracy. In addition, we introduce a significance score allowing examiners to set a threshold and identify related documents.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Identifying document similarity has many applications, e.g., source code analysis or plagiarism detection. background 1.00 0.04 0.02 asserted 1.00
s1 However, identifying similarities is not trivial and can be time complex. background 0.86 0.05 0.02 asserted 0.88
s2 For instance, the Levenshtein Distance is a common metric to define the similarity between two documents but has quadratic runtime which makes it impractical for large documents where large starts with a few hundred kilobytes. background 0.90 0.06 0.03 asserted 1.00
s3 In this paper, we present a novel concept that allows estimating the Levenshtein Distance: the algorithm first compresses documents to signatures (similar to hash values) using a user-defined compression ratio. contribution 0.57 0.91 0.14 asserted 1.00
s4 Signatures can then be compared against each other (some constrains apply) where the outcome is the estimated Levenshtein Distance. method 0.99 0.74 0.08 asserted 0.97
s5 Our evaluation shows promising results in terms of runtime efficiency and accuracy. result 0.99 0.96 0.57 asserted 0.86
s6 In addition, we introduce a significance score allowing examiners to set a threshold and identify related documents. contribution 0.98 0.94 0.11 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.71.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Identifying document similarity has many applications, e.g., source code analysis or plagiarism detection. However, identifying similarities is not trivial and can be time complex. For instance, the Levenshtein Distance is a common metric to define the similarity between two documents but has quadratic runtime which makes it impractical for large documents where large starts with a few hundred kilobytes. In this paper, we present a novel concept that allows estimating the Levenshtein Distance: the algorithm first compresses documents to signatures (similar to hash values) using a user-defined compression ratio. Signatures can then be compared against each other (some constrains apply) where the outcome is the estimated Levenshtein Distance. Our evaluation shows promising results in terms of runtime efficiency and accuracy. In addition, we introduce a significance score allowing examiners to set a threshold and identify related documents.

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