# Explainable Identification of Similarities Between Entities for Discovery in Large Text
## 1 Introduction  
In the modern digital landscape, the need for effective methods to analyze, compare, and interpret large textual data is paramount. The ability to detect patterns of similarities between text documents can provide immediate insights that would otherwise be difficult to make. For instance, users who study certain political figures can use large amounts of text to identify certain similarities between them. Researchers in the medical domain can identify similarities or common causes between two clinical conditions by comparing a large corpus of medical records that describe these conditions. Similarly, health records associated with certain drugs or vaccines can be compared to identify symptoms or side effects, which can be analyzed even by scanning user-generated documents through social media. In the humanities domain, comparisons can be used to identify specific similarities between novels, literary styles, or characters. However, despite the potential usefulness of the ability to quickly perform an explainable identification of similarities between documents, such algorithms have attracted relatively little attention. Automatic analysis of text has been studied intensively for tasks such as automatic document classification [1, 2, 3, 4], automatic translation of documents [5, 6], document summararization [7; 8; 9], and automatic generation of documents, which is often performed through large language models [10]. Text document comparison is a common feature in modern word processors, but its goal is merely to compare and match words and sentences in the body of the text. It cannot be used effectively for comparing specific topics of similarities discussed in the documents. Therefore, while automatic document comparison is commonly used feature, it is irrelevant to the task of identifying specific similarities between two or more entities as they are studied through the text documents. One of the most common forms of identifying similarities between documents is automatic plagiarism detection [11; 12; 13; 14; 15]. Other research efforts focused on similarities between scientific documents, including similarities in keywords and references [16]. Term extraction was also used to determine document similarities [17]. Domains in which automatic document similarities are commonly used include law [18; 19] and accounting [20]. Using machine learning, the task of clustering was applied to text documents to identify the most similar documents inside a text corpus [21]. Similarities between text documents can also be identified using stylistic elements of the writing [22; 23]. Document similarity comparison can include string-based methods, knowledge-based methods, corpus-based methods, and hybrid methods that combine different approaches [24]. String-based similarities aim at lower level comparison of the text by comparing two or more sequences of letters. For instance, that can be achieved by the minimum number of primitive operations required to transform one strong into the other [25; 26; 27]. The approach of edit distance has been used intensively also outside the task of text comparison, for instance in field of genetics to compare and identify matching DNA sequences [28]. Knowledge-based methods use semantic networks to extract information and make conclusions from text. It uses pre-defined ontologies to make connections between meaning and words, often in specific predefined domain. The difference between two pieces of text can be measured through the path on the semantic network that needs to be taken in order to lead to a match [29; 30]. Another form is through collections of common terms or concepts [31; 32]. Corpus-based similarity of text is based on applying similarities between sub-parts of the text, normally sentences, and a defined collection of text documents. The comparison to single yet very large set of text documents allows to find similarities to common sentences that appear in the corpus, which allows to identify similarities or to transform text documents [33; 34; 35].