## 2 Background and Relevant Literature
### Keyword Extraction  
Here, we refer to a keyword as a term that constitutes a single word (e.g., _temperature_) or multiple words (e.g., _cooling systems_). When publishing, authors are often required to assign up to five or six keywords to the research article. These keywords tend to be general and mainly for catalog purposes. Keyword extraction techniques have been developed to automatically identify a list of terms that provide a more comprehensive summary of a document, and it is a technique widely used in information retrieval and topic modeling. The common approaches ([PERSON] et al., 2020) to derive keywords include:  
* Rule-based linguistic approaches that use linguistic knowledge and rules;
* Statistical approaches (such as TF-IDF) that use term frequency and co-occurrence statistics;
* Domain knowledge approaches that utilize an ontology in a domain to identify keywords;
* Machine learning approaches that use algorithms to automatically detect keywords.  
Hence, our literature reviews focus on machine learning approaches that are used in this study. The machine learning approaches are of two types: supervised and unsupervised. Supervised approaches train models using human-labeled keywords. The KEA (Keyphrase Extraction Algorithm) model ([PERSON] et al., 2019) is one of the most used and was built using author-assigned keywords to identify relevant and non-relevant candidate keywords. However, the supervised approach requires human-labeled data, which is not always available ([PERSON] et al., 2020). There are publicly accessible topic datasets labeled with keywords in the areas of public health, biomedicine, and pandemic impact, provided by research analytics tools such as the SciVal service ([PERSON] et al., 2020), but they are not particularly useful for this study, which focuses on different domains (e.g., machine learning and urban planning).  
In contrast, unsupervised approaches use rank algorithms to select the representative keywords from a list of candidates. For example, TextRank ([PERSON] et al., 2019) uses a word graph to represent a document, where the nodes are nouns or verbs and their occurrences within a window (2 to 10 words) are edges. Keywords are selected using Google's PageRank algorithm ([PERSON] et al., 2019). Rake ([PERSON], 2019) (Rapid Automatic Keyword Extraction) uses the word statistics (word frequency and word co-occurrences) of a document to determine the keywords from a list of content words that convey the meanings and exclude punctuations and function words such as and, of, and the. YAKE! ([PERSON] et al., 2019) calculates and ranks candidates (one or two words) by a combined score of word statistical features, such as word case (uppercase is more important), position, frequency, relatedness to context, and occurrences in different sentences.  
Nowadays, unsupervised algorithms are the most commonly used because they can be applied to the texts in different domains and languages, especially when human annotated labels are not available. However, they tend to generate similar candidate keywords such as _deep learning analytics, learning technique_, and _machine learning_ that express a similar concept and therefore reduce the informativeness and representativeness of the keyword list [42]. In this study, we incorporate the Maximal Marginal Relevance (MMR) [43] ranking algorithm to select candidate keywords and remove redundant candidates that have similar meanings from the keyword list. The MMR algorithm has been adopted in information retrieval and recommendation systems to provide relevant and interesting results that users have preferences over [44].