A Comparative Assessment of Unsupervised Keyword Extraction Tools

Nadim, Mohammad; Akopian, David; Matamoros, Adolfo

doi:10.1109/access.2023.3344032

Cited by 3 publications

(1 citation statement)

References 66 publications

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“…Keywords, defined as single or multiple word units, are employed to express the central theme of a text [1]. The extraction of keywords plays a pivotal role in applications such as information retrieval, text summarization, automatic categorization, clustering, and indexing [2].…”

Section: Introductionmentioning

confidence: 99%

An Improved TextRank Keyword Extraction Method Based on the Watts-Strogatz Model

Li,

Zhang,

Khadka

2024

IDA

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Traditional methods for keyword extraction predominantly rely on statistical relationships between words, neglecting the cohesive structure of the extracted keyword set. This study introduces an enhanced method for keyword extraction, utilizing the Watts-Strogatz model to construct a word network graph from candidate words within the text. By leveraging the characteristics of small-world networks (SWNs), i.e., short average path lengths and high clustering coefficients, the method ascertains the relevance between words and their impact on sentence cohesion. A comprehensive weight for each word is calculated through a linear weighting of features including part of speech, position, and Term Frequency-Inverse Document Frequency (TF-IDF), subsequently improving the impact factors of the TextRank algorithm for obtaining the final weight of candidate words. This approach facilitates the extraction of keywords based on the final weight outcomes. Through uncovering the deep hidden structures of feature words, the method effectively reveals the connectivity within the word network graph. Experiments demonstrate superiority over existing methods in terms of precision, recall, and F1-measure.

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Section: Introductionmentioning

confidence: 99%

An Improved TextRank Keyword Extraction Method Based on the Watts-Strogatz Model

Li,

Zhang,

Khadka

2024

IDA

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Unsupervised extraction of local and global keywords from a single text

Aleksanyan,

Allahverdyan

2024

Nat. lang. processing

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We propose an unsupervised, corpus-independent method to extract keywords from a single text. It is based on the spatial distribution of words and the response of this distribution to a random permutation of words. Our method has three advantages over existing unsupervised methods (such as YAKE). First, it is significantly more effective at extracting keywords from long texts in terms of precision and recall. Second, it allows inference of two types of keywords: local and global. Third, it extracts basic topics from texts. Additionally, our method is language-independent and applies to short texts. The results are obtained via human annotators with previous knowledge of texts from our database of classical literary works. The agreement between annotators is moderate to substantial. Our results are supported via human-independent arguments based on the average length of extracted content words and on the average number of nouns in extracted words. We discuss relations of keywords with higher-order textual features and reveal a connection between keywords and chapter divisions.

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Detailed Image Captioning and Hashtag Generation

Shetty,

2024

Future Internet

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This article presents CapFlow, an integrated approach to detailed image captioning and hashtag generation. Based on a thorough performance evaluation, the image captioning model utilizes a fine-tuned vision-language model with Low-Rank Adaptation (LoRA), while the hashtag generation employs the keyword extraction method. We evaluated the state-of-the-art image captioning models using both traditional metrics (BLEU, METEOR, ROUGE-L, and CIDEr) and the specialized CAPTURE metric for detailed captions. The hashtag generation models were assessed using precision, recall, and F1-score. The proposed method demonstrates competitive results against larger models while maintaining efficiency suitable for real-time applications. The image captioning model outperforms the base Florence-2 model and favorably compares with larger models. The KeyBERT implementation for hashtag generation surpasses other keyword extraction methods in both accuracy and speed. This work contributes to the field of AI-assisted content analysis and generation, offering insights into the practical implementation of advanced vision-language models for detailed image understanding and relevant tag generation.

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A Comparative Assessment of Unsupervised Keyword Extraction Tools

Cited by 3 publications

References 66 publications

An Improved TextRank Keyword Extraction Method Based on the Watts-Strogatz Model

An Improved TextRank Keyword Extraction Method Based on the Watts-Strogatz Model

Unsupervised extraction of local and global keywords from a single text

Detailed Image Captioning and Hashtag Generation

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