# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
## 2 Overview of State-of-the-Art Approaches
### Content-Based Approaches
This category representation saved them effort and time, as they did not need to deal with all training documents and categories of different levels. They concluded that there is a need for an effective feature selection technique with the diversified dataset for the text classification [34; 35]. [PERSON] and [PERSON] [36] proposed an approach to assign a scientific document to one or more classes which is called multi-label hierarchy by using the content of the scientific documents. A similar approach was presented by [PERSON] [37], based on ranking category relevance to evaluate the multi-label problems. Another similar approach is proposed by [PERSON] and [PERSON] [38] for the CINDI digital library. They formulated their method for ranking classes on the same level which can be helpful for text classification. The evaluation of this approach was performed on the collected dataset by using ACM98 classification scheme. They extracted research articles from the ACM digital library belonging to the computer science domain. Their method of text classification specifies and prepares the rank for the categories at the same level. Their method works from top downwards in the hierarchy until the suggested category is assigned. They used a flat local multi-label classifier which served as the basic block in their hierarchical classification system. [PERSON] and [PERSON] [39] presented another hierarchical approach to classify text documents by using an SVM classifier. They exploited the relationships among the classes which are commonly expressed in the form of hierarchy. [PERSON] and [PERSON] [40] proposed a technique for the classification of text documents based on text similarity. They presented a feature selection framework which calculates the score of selected words for text classification. They have also presented a learning model for text categorization, in which document collections were randomly selected and annotated by the domain experts. The evaluation of this classification approach is also presented by [41]; they evaluated different classification approaches with their merits and demerits [42; 43; 44]. [PERSON] et al. [45] presented a systematic evaluation of classification approaches to explore how far semantic annotations can be conducted using just the metadata of the documents. The evaluation was completed with the classification obtained from analyzing only the metadata and with analyzing the semantic annotation of the whole text. [PERSON] et al. [46] proposed a multi-label document-ranking model based on Long Short-Term Memory (LSTM). It consisted of two processes, one was repLSTM (an adapted representation process) and the other one was rankLSTM (a unified learning ranking process). Three datasets were used for the experiments to classify documents with reasonable performance of their proposed model. [PERSON] and [PERSON] [47] presented a method which performed hierarchical multi-label document classification by initializing a neural network model. They evaluated their approach on the biomedical domain using both sentences and document level classification. [PERSON] et al. [48] proposed an ensemble classification method which groups together random forest and semantic core co-occurrence latent semantic vector space (CLSVSM). The Yahoo dataset was used for experiments which revealed the effectiveness of the proposed method with reasonable results. In the work by [49; 50; 51; 52; 53], the authors proposed automated text classification/categorization approaches. The document classification community is dominated with the content-based approaches. Of course, these approaches have richness in terms of features and produce promising results. To make these schemes applicable to the content of the documents is a vital requirement but most of the digital libraries are subscription-based such as ACM, IEEE, Springer, etc. [54; 55; 56; 57]. There is a need for some alternative method to categorize documents when the content is not available.