# Unlocking the Potential of Keyword Extraction: The Need for Access to High-Quality Datasets
## 1 Introduction  
Although there has been a significant advancement in recent years, the challenge of extracting significant keywords remains unresolved. Current algorithms for keyword extraction are not as efficient as those in many other fundamental domains of computer science, indicating that there is still room for improvement. The majority of established methods of deep learning typically involve a supervised methodology that is reliant on the availability of annotated text corpora for effective implementation. One of the initial methods for keyword extraction was presented by [PERSON] [1], who created a specialized algorithm known as GenEx. Supervised techniques, such as Naive Bayes, have been the primary method for identifying pertinent keywords in the majority of the approaches developed for keyword extraction. KEA [2] is perhaps the most widely used implementation of this approach, utilizing the Naive Bayes machine learning algorithm for extracting keywords. Supervised methods, which are frequently more successful, suffer from the main limitation of having a relatively long training period. In contrast, unsupervised algorithms do not have the same training time constraints as supervised methods [3; 4; 5]. Unsupervised algorithms can easily be applied to documents in different languages or domains with little effort, as they are plug-and-play and can be executed quickly. However, unsupervised learning does not require labeled data. Instead, they use statistical patterns and relationships in the data. For example, in keyword extraction, unsupervised algorithms analyze the frequency and co-occurrence of words in a text corpus to identifythe most significant terms [3; 4; 5]. However, to achieve reliable and complete results, the quality of the input data is critical. If the data is noisy, incomplete, or inconsistent, the unsupervised algorithms may produce inaccurate or irrelevant results. For example, if the text contains spelling errors or non-standard abbreviations, the algorithm may miss important keywords or include irrelevant ones. Similarly, if the text is missing important context or background information, the algorithm may not be able to identify the most relevant keywords. Therefore, it needs to identify such data that is complete, error-free, and produces accurate results for the algorithms. Moreover, the availability of a high-quality, diverse dataset for keyword extraction could have a significant impact on the development of machine learning algorithms and natural language processing techniques.  
Improved accuracy: A diverse dataset with high-quality data would allow researchers to develop more accurate machine-learning algorithms for keyword extraction. With access to a larger variety of text types and language structures, models could be trained to recognize patterns and relationships between keywords and text more effectively.  
Generalizability: A diverse dataset would enable the development of keyword extraction models that are more generalizable across different domains and languages. This would be especially important for real-world applications, where models must be able to handle a wide range of text types and contexts.  
Better evaluation: With access to a high-quality, diverse dataset, researchers could more effectively evaluate the performance of different keyword extraction models. This would make it easier to identify which models are most effective and pinpoint areas for improvement.  
Increased innovation: A high-quality, diverse dataset would provide a foundation for new innovation in keyword extraction. Researchers would be able to build on existing work and develop new techniques and approaches for more effective semantic analysis.