## 1 Introduction and Literature Survey
### Related Work  
Multiple-label data annotation has long attracted significant attention from the machine learning research community. [PERSON] and [PERSON] [27] gave a comprehensive survey of early work in this direction. The authors stayed short of experimental comparison of different methods. They mainly discuss systems built around learning models, such as Support Vector Machine (SVM), \(k\)-nearest neighbor, logistic regression, and the like. In a related study, [PERSON] et al. [28] demonstrated that LDA-based approaches generally outperform discriminative methods in the case of datasets with many labels. Subsequent studies resulted in an array of probabilistic generative models (e.g., MLTM [29], SMTM [30]), allowing for data annotation in a semi- or \"weakly\"-supervised mode. As a rule, the developed LDA-based approaches require label-topics or seed keywords to be manually selected in the beginning of the annotation process. The latter may be non-trivial, if feasible at all, in the case of e-commerce data, as multiple-topic manual labeling of large volumes of short texts was shown to produce highly subjective and contradictory results (e.g., see [31]). [PERSON] et al. [32] proposed an optimization algorithm called Adaptive Labeled LDA (AL-LDA) to deal with label disparity. The developed system outperformed many other state-of-the-art multiple-label classification models and worked satisfactory even under massive label noise. An obvious drawback of this approach is its computational cost that would grow enormously in the case of processing large volumes of constantly flowing data. It should be noted that over the years, several classifier-based systems to assist or automate multiple-label annotation have been developed as well. Largely acceptable results were obtained by applying reinforcement learning (sentence-level labeling of dialogs [33]), deep learning (in combination with LDA [34]) and embedding learning [35]. However, all of these discriminative approaches suffer from drawbacks not unlike the ones pointed to earlier: relying on taken-for-granted human expertise and relatively high computational cost in the case of multiple-label classification. Furthermore, differently from generative models, classifier-based methods provide little, if any, facilities to accommodate for new, previously unseen topics. The latter makes these methods a poor fit for processing dynamic data volumes of, for instance, online customer reviews. Text segmentation has been used to identify boundaries of semantic units, such as sentences, paragraphs, and news, to mine opinions and emotions, evaluate sentiment, determine language, etc. See [26] for a representative survey of TS applications. Early text segmentation algorithms had a low complexity and often were data- or task-specific, computing segment boundaries based on text similarity metrics (e.g., see [36]). [PERSON] and [PERSON] [24] developed a method to break a document into coherent segments corresponding to document themes, thus allowing for multi-topic document clustering. Identifying segment topics was left unaddressed. [PERSON] et al. [37] proposed a recursive soft clustering algorithm with built-in topic segmentation for legal document processing. Performance was evaluated in comparison with legal experts. The algorithm utilizes document metadata and requires human involvement to define clusters. In a recent study, [PERSON] et al. [38] applied LDA for text segmentation, determining segment boundaries by topic coherence score. As an alternative, [PERSON] et al. [39] suggested to learn segment bounds directly from labeled data. The authors used a model pre-trained on Wikipedia texts to break data into semantically consistent pieces. [PERSON] and [PERSON] [25] proposed an iterative algorithm built around a trained classifier to perform text segmentation on multiple-label documents. In each iteration, the algorithm works to refine the segmentation of training data to obtain a more accurate classification model.