## 3 Methodology
### Keywords Extraction  
This phase identifies a number of keywords to represent an abstract in each abstract cluster. Keyword extraction is commonly used to generate a summary of a text or to build indexes for browsing a collection of documents. In this study, we define the keywords as the descriptive terms that capture the meaning of an abstract and they are nouns and compound nouns made of two to six words. Figure 3 illustrates the three-step procedure. Step 1 identifies a list of candidate words from an abstract. The Stanford CoreNLP toolkit [54] splits an abstract text into individual sentences and tokenizes each sentence to words that are then lowercase and lemmatized (e.g., \"_Studies_\" to \"_study_\"). The Stanford POS tagger parses a sentence and assigns a Part of Speech (POS) tag to individual words according to their syntactic role, e.g., \"adjective\", \"noun\", or \"verb\". Compound nouns are identified by matching the POS tags of words in a sentence against a set of predefined syntactic patterns as shown in Table 1, where NN stands for a noun and JJ an adjective, and bracket [] means optional. For instance, in the tagged sentence:  
_We_PRP integrate_VBP **visualization_NN techniques_NN**_with_IN machine_NN **learning_NN models_NNS to_TO facilitate_VB the_DT detection_NN of_IN **street_NN **view_NN patterns_NNS**The three identified candidate words are _visualization technique, machine learning model_, and _street view pattern_ as they all follow the pattern NNs (two or more nouns) in Table 1.  
Step 2 measures the similarities between a candidate word and the abstract it comes from to determine the representativeness. The candidate words and abstracts are converted into the GPT-3 embeddings. The pairwise cosine similarity is calculated for each pair of candidate words and abstracts. Then, the candidate words are ranked by their similarity to the abstract. Step 3 selects five candidate words using the Maximal Marginal Relevance (MMR) algorithm [43] to form the list of keywords of an abstract. MMR selects keywords that are highly similar to the abstract but have a low similarity to other keywords so that the keyword list contains fewer keywords that have similar meanings. Taking the abstract shown in Figure 4 as an example, the top five candidate words ranked by similarity to the abstract are _urban heat island research_, _urban heat island study_, _urban heat island phenomenon_, and _urban heat island_, where the word _urban heat island_ repeatedly appears in all five candidate words. Using MMR, _urban heat island research_, _land parcel_, _machine learning approach_, _important variable_, _physical and socioeconomic characteristic_ are chosen as the final five keywords to represent the abstract. Figure 4 also includes the keywords that authors provide. This result shows a certain overlapping (_urban heat island effect_, _machine learning_, and _ socioeconomic vulnerability_) between author keywords and auto generated keywords. The differences \begin{table}
\begin{tabular}{c c c} \hline \hline
**Pattern** & **Description** & **Examples** \\ \hline NNs & two or more nouns & _land parcel, planning practice, machine learning approach, land surface temperature_ \\ \hline JJ + NNs & one adjective plus one or more nouns. & _important variable, main contribution, urban heat island, urban heat island research_ \\ \hline JJ + [and] + JJ + NNs & one adjective plus an optional conjunction _and_ plus one adjective plus one or more nouns. & _relevant spatial scale, physical and socioeconomic characteristic, major urban environmental problem_ \\ \hline \multirow{2}{*}{[JJ] + NN + and + NNs} & an optional adjective plus a noun plus the conjunction _and_ plus one or more nouns. & _air and noise pollution, urban greenery and planning practice, urban planning, and land use management_ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Syntactic patterns for candidate words and examples. Figure 3: The keyword extraction procedure.