@article {
    orvium-650f02fc34dc44d0f1df29a6,
    title = "Automatic Keyword Extraction: a literature review",
    abstract = "Automatic Keyword Extraction (AKE) is
a fundamental Natural Language Processing (NLP) task that plays a critical role
in various applications, including information retrieval, document summarization,
and content categorization. The review’s
purpose is to gather all the techniques,
methodologies, and advancements available in the literature, and present them
to researchers, practitioners, and developers interested in the topic, who can use
this work as a starting point for their research, or to have a quick insight on what
are the main trend in the automatic keyword extraction task. The review provides an in-depth analysis of traditional
approaches, supervised and unsupervised,
as well as emerging techniques that employ neural networks and deep learning architectures like transformers. This work
also provides information regarding existing datasets and benchmarks and how to
obtain them, as well as functioning code
for building practical applications. At the
end of the paper, there is a description of
the state-of-the-art for automatic keyterm
extraction, paired with a discussion on the
evaluation metrics.",
    keywords = "automatic keywords extraction, key phrases, natural language processing, machine learning, deep learning, supervised learning, unsupervised learning, transformers, key terms",
    author = "Matteo Mortella",
    year = "2023",
    language = "English",
    url = "https://dapp.orvium.io/deposits/650f02fc34dc44d0f1df29a6/view",
}