## 5 Discussion  
It should be noted that there are a number of limitations to our approach. For instance, we can only work on short texts because of the input size restriction of the GPT-3 similarity embedding models. At the moment, the maximum length of input text for the GPT-3 embedding models is 2048 tokens, which is sufficient for publication abstracts but not for the entire paper. The main factor that affects the accuracy of the document clustering results is the quality of the dense representation of the semantic meaning of the documents. Our approach relied on pre-trained embedding models that are typically trained on general purpose text data such as Wikipedia articles, books, or web pages. Some domain-specific models such as BioBERT [58] that fine-tune the BERT model on a small in-domain corpus such as scientific publications in a certain area are proposed and have provided significant improvement of domain-specific tasks. However, training or fine-tuning a GPT-3 similarity embedding model for this study is beyond our capacity. Another issue faced during this study was how to handle outliers. The occurrences of outliers among the abstracts are unavoidable as they are retrieved from the Scopus database using two general terms: _machine learning_ and _urban study_ without manual filtering. This is evidenced by the fact that about 48.3% (287/593) of the abstracts were marked as outliers during the first clustering iteration (Table 2 in Section 4.1). One of the options for dealing with outliers is to discard them, which means abandoning almost half of our dataset. Instead, we chose to feed them as one cluster to the second iteration because some of the outliers are the data points falling on cluster borders. The results in Table 2 show that eight clusters (#12, #13, #14, #15, #17, #18, #19, #20) with reasonable silhouette scores (above 0.5) are generated in the subsequent iterations from outliers identified in the first iteration, which proves that our iterative clustering approach is practical and useful. In addition, we used the unsupervised silhouette measure to evaluate the clustering results as it does not require training on ground-truth data to learn the formation of a cluster. This measure uses the distance metric to evaluate the clustering quality, whilst the HDBSCAN clustering algorithm we used is density-based. Having different metrics is common in clustering algorithms and evaluations [59] because the evaluation should have its own standard and have no preference for any algorithm. However, it would be interesting to experiment with the density-based silhouette score such as the one proposed by [PERSON] (2011) [60], which may help estimate the inconsistency in the clustering results caused by the use of different measures. Our keywords extraction approach, inspired by BERT-Topic [56] and KeyBERT [57], utilizes the new development of the GPT-3 similarity embeddings and modern document clustering techniques (HDBSCAN). The quality of extracted keywords is determined by two factors: the pre-trained embedding models that are used to convert keywords to embeddings that represent the semantic meaning of the text and the selection criteria. The explanation of the keyword grouping can be demonstrated with our visualization. For example, in Figure 6a, two keywords _city bus_ and _Dengue dynamic_ in group 2 are distinct from the others (such as the _Cairo region_, _Vienna_, and _Los Angeles_). Through our keyword grouping visualization, we can retrieve the abstracts that these keywords come from and examine the sentences where these keywords appear to help understand the contexts of the keywords in the texts. For the second factor, we used the Maximal Marginal Relevance (MMR) algorithm coupled with GPT-3 embedding to rank and select five keywords in each document. MMR's diversity parameter, with a value between 0 and 1, controls the relevance (similarities to the document) and the keyword diversity (similarities among keywords), and the diversity of 0.5 places equal importance on relevance and diversity.