## 1 Introduction  
With the number of research scientific publications growing rapidly over the past decades, it has become increasingly difficult for researchers to review large volumes of publications to discover the current topics and explore new emerging themes in a scientific field. Topic detection is a process that identifies the main topics among a collection of text data, where each topic is represented by a list of words [1]. LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis) are popular topic modeling techniques that help to discover, summarize, and represent topics from a large corpus of texts. These methods are commonly used in the analysis of trending topics in social media texts [2]. In recent years, the demand for topic detection tools that can find topics but also help understand how a topic is derived has become increasingly important. Hence, keywords, of up to five or six words, have been commonly used to represent the topics of a document in the literature. They have also served as index terms for searching published literature in bibliographic databases such as Scopus, Web of Science, ScienceDirect, etc. Existing bibliometric text analysis tools, such as SciMAT [3], bibliometrix [4], and VOSviewer [5], use keywords to provide a general idea of the scientific publications under investigation. Most keywords are single words, but two or three words are also common. They are usually assigned by authors based on their own judgment by means of common taxonomy. However, more recently, the advancement of text mining techniques has automated the keyword extraction process, particularly from a large volume of texts. Current keyword extraction methods fall into two categories: supervised and unsupervised. The supervised approaches, for example, KEA [5], treat keyword identification as a classification task. It trains a model on top of an external corpus with known keywords to distinguish relevant and irrelevant keywords and the model is then used to predict the keywordness of candidate words. Unsupervised learning approaches such as YAKE! [6], TextRank [7], or RAKE [8] use algorithms to rank candidate words, from which keywords with high ranks are selected. These methods treat the collection of documents as a whole when generating keywords. There has been research that combines document clustering and keyword algorithms to detect topics, where the documents are split into a number of clusters and topics or keywords are extracted for each cluster. This approach represents a document with a vector of float numbers (also called _an embedding_), which quantifies the semantic meaning of a document, and then generates clusters based on the semantic similarity of documents so that documents with similar meanings are grouped in the same cluster. The most popular clustering algorithms are K-means and HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise), where K-means requires a predetermined K, the number of clusters, and HDBSCAN can detect the outliers, the documents not belonging to any clusters. There are a number of document embedding methods, from Bag-of-words (BoW), Word2 Vec [9], and Doc2 Vec [10] to the most recent, transformed-based such as BERT (Bidirectional Encoder Representations from Transformers) [11] and the GPT-3 similarity embeddings [12]. [PERSON] et al. (2020) [13] and [PERSON] et al. (2021) [14] experimented with Doc2 Vec embedding with off-the-shelf clustering algorithms, such as K-means, hierarchical agglomerative clustering, and deep embedded clustering [15], on publication abstracts and then used the TF-IDF terms to label each cluster. Their results showed that the use of Doc2 Vec embedding improves the accuracy of clustering algorithms. However, there is limited research in the area of topic analysis that takes advantage of the transformer-based language models such as BERT and GPT-3 to represent the semantic meaning of the text.