# Unlocking the Potential of Keyword Extraction: The Need for Access to High-Quality Datasets
## 3 Dataset Identification for Keyword Extraction
### Mohler Dataset
#### 3.2.3 Dataset Quality
YAKE also assigned relatively high scores to the keywords \"simulate\", \"behavior\", \"desired\", and \"portions\", while RAKE assigned the lowest scores to all keywords. We again identify the most mixed results by extracting the two grams from the reference answers. We have noticed some slight changes. In Table 6, KeyBERT extracts the top five bi-grams with high scores. However, YAKE extracts the top five keywords but also generates duplicates such as \"Desired\", and \"Desired portions\" with a lower score than KeyBERT; in order to do this, RAKE was unable to remove the stop words and extract the top five bi-grams with stop words such as\" the behavior\", and \"of portions\" and generates a low score. Exploring the Suitability of KeyBERT, YAKE, and RAKE for Keyword Extraction across Datasets with Dataset Structure, Complexity, and Quality  
Table 7 compares three different models for automated keyword extraction: KeyBERT, YAKE, and RAKE in terms of the dataset's structure, complexity, and quality. KeyBERT is best suited for datasets with a structured format and a standardized question-answer format. It works well on datasets with technical terms, domain-specific jargon, and high-quality context. YAKE works well on both structured and unstructured datasets but is best suited for datasets with domain-specific jargon [36; 37]. However, it cannot identify technical terms or detect background information. RAKE also works well on both structured and unstructured datasets, but is best suited for datasets with straightforward language complexity. It can handle datasets with lower-quality data but is unable to remove noise from the text. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**KeyBERT**} & \multicolumn{3}{c}{**Mahler Dataset**} & \multicolumn{1}{c}{**RAKE**} \\ \hline
**Key Phrase (Bi-Gram)** & **Score** & **Key Phrase (Bi-Gram)** & **Score** & **Key Phrase (Bi-Gram)** & **Score** \\ \hline Software product & 0.801 & Software product & 0.301 & Software product & 0.102 \\ Simulate behavior & 0.793 & Simulate behavior & 0.321 & the behavior & 0.493 \\ Desired software & 0.568 & Desired & 0.108 & Desired software & 0.234 \\ Desired portions & 0.743 & Desired portions & 0.240 & of portions & 0.323 \\ Simulate product & 0.835 & Simulate product & 0.435 & product & 0.165 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: KeyBERT, YAKE, and RAKE results with bi-gram. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Dataset Structure** & **Dataset Complexity** & **Dataset Quality** \\ \hline KeyBERT & \(\bullet\) & Works well on structured datasets & \\  & with a standardized & & \\  & question–answer format. & & Works well on datasets with \\ KeyBERT & \(\bullet\) & Has the potential to outperform & domain-specific jargon and \\  & YAKE and RAKE on structured & & technical terms. & high-quality context. \\  & datasets due to its ability to & & \\  & leverage context. & & \\ \hline YAKE & \(\bullet\) & Works well on both structured & \\  & and unstructured datasets. & & Works well on datasets with \\  & & and & domain-specific jargon but is unable \\  & & to identify the technical terms. & background information. \\ \hline RAKE & \(\bullet\) & Works well on both structured & \\  & and unstructured datasets. & & Works well on datasets with \\  & & and & straightforward language \\  & & complexity. & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of automated keyword extraction of KeyBERT, YAKE, and RAKE models.