A Fast Text Similarity Measure for Large Document Collections using Multi-reference Cosine and Genetic Algorithm

TL;DR

This paper introduces a fast, scalable, and reliable text similarity measure for large document collections using multi-reference cosine signatures optimized by genetic algorithms, improving duplicate detection efficiency.

Contribution

It presents a novel signature-based text similarity method that employs genetic algorithms to generate optimal reference texts, enhancing duplicate detection in large datasets.

Findings

Comparable accuracy to state-of-the-art algorithms

High scalability and speed in large datasets

Reduced storage requirements

Abstract

One of the important factors that make a search engine fast and accurate is a concise and duplicate free index. In order to remove duplicate and near-duplicate documents from the index, a search engine needs a swift and reliable duplicate and near-duplicate text document detection system. Traditional approaches to this problem, such as brute force comparisons or simple hash-based algorithms are not suitable as they are not scalable and are not capable of detecting near-duplicate documents effectively. In this paper, a new signature-based approach to text similarity detection is introduced which is fast, scalable, reliable and needs less storage space. The proposed method is examined on popular text document data-sets such as CiteseerX, Enron, Gold Set of Near-duplicate News Articles and etc. The results are promising and comparable with the best cutting-edge algorithms, considering the accuracy and performance. The proposed method is based on the idea of using reference texts to generate signatures for text documents. The novelty of this paper is the use of genetic algorithms to generate better reference texts.