## 2 Background
### Content-Based Similarity Measures  
Many similarity measures work on the main contents of articles, including titles, abstracts, and main texts. We are concerned with those that worked on the _titles_ and _abstracts_ (TA) of articles, as they are much more publicly available than main texts, making the clustering systems applicable to most scholarly articles.  
The vector space model (VSM) is a typical technique to estimate content-based similarity. It represents each article as a vector of term weights, and similarity between two articles is simply the cosine similarity on their vectors. VSM was employed by many scholarly article retrieval systems (e.g., [17]). However, cosine similarity did not always perform well [18; 19]. Latent Semantic Analysis (LSA) was a typical technique that employed singular value decomposition to improve the vector representation of scholarly articles [20; 21]. However LSA may not perform well for scholarly articles either [19].  
_OK_ was developed based on _BM25_[22], which was one of the best techniques for finding related scholarly articles [19]. It was shown to be one of the best techniques to identify related articles as well [18]. _OK_ employs Equation (4) to estimate the similarity between two articles, \(a_{1}\) and \(a_{2}\), where \(k_{1}\) and \(b\) are two parameters, \(\left|\,a\,\right|\) is the number of terms in article \(a\) (i.e., length of \(a\)), _avgal_ is the average number of terms in an article (i.e., average length of articles), _TF_(\(t\),\(a\)) is the frequency of term \(t\) appearing in article \(a\), and _IDF_(\(t\)) is the inverse document frequency of term \(t\), which measures how rarely \(t\) appears in a large collection of articles.  
\[OK(a_{1},a_{2}) = \sum_{t\in a_{1}\cap a_{2}}\,\frac{TF(t,a_{1})(k_{1}+1)}{TF(t,a_{ 1})+k_{1}(1-b+b\frac{\left|a_{1}\right|}{avgal})}\,\frac{TF(t,a_{2})(k_{1}+1)} {TF(t,a_{2})+k_{1}(1-b+b\frac{\left|a_{1}\right|}{avgal})}Log_{2}IDF(t) \tag{4}\]  
PubMed is a popular search engine for biomedical scholarly articles. It estimates similarity between two scholarly articles by considering several kinds of information [23; 24], including (1) term stemming, (2) article lengths, (3) term positions in the articles (e.g., terms in titles of the articles), (4) key terms of the articles, and (5) weights of the terms in the articles (including _TF_ and _IDF_). The similarity measure used by PubMed to estimate inter-article similarity is summarized in [23]. It was found to be one of the best to cluster scholarly articles [19].  
Another kind of similarity measure can be derived by _topic modeling_, which identifies latent topics embedded in a collection of articles. An article can be a mixture of topics, and hence, it can be represented as a topic proportion vector in which a dimension corresponds to the degree of relatedness to the corresponding topic (for a review of probabilistic topic modeling techniques, the readers are referred to [25]). Similarity between articles can be estimated based on the topic proportion vectors of the articles; hence, by treating the topics as clusters of articles, various topic modeling techniques were applied to the clustering of scholarly articles [26] and news articles [27].  
However, none of these similarity measures aims at issue-based clustering, as it is quite difficult to identify research issues from the main contents of articles. ICRT recognizes the research issues by analyzing the titles of the references cited by the articles, based on the observation that, to discuss a research issue, an article often cites related references whose titles may indicate the issue. We will implement several systems that employ typical similarity measures to conduct clustering (see Section 4.2), and evaluate how these systems can be further improved to conduct issue-based clustering by employing ICRT as a post-processor for them.