Deep learning and embeddings-based approaches for keyphrase extraction: a literature review

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Nikolaos Giarelis, Nikos Karacapilidis
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Abstract

Keyphrase extraction is a subtask of natural language processing referring to the automatic extraction of salient terms that semantically capture the key themes and topics of a document. Earlier literature reviews focus on classical approaches that employ various statistical or graph-based techniques; these approaches miss important keywords/keyphrases, due to their inability to fully utilize context (that is present or not) in a document, thus achieving low F1 scores. Recent advances in deep learning and word/sentence embedding vectors lead to the development of new approaches, which address the lack of context and outperform the majority of classical ones. Taking the above into account, the contribution of this review is fourfold: (i) we analyze the state-of-the-art keyphrase extraction approaches and categorize them upon their employed techniques; (ii) we provide a comparative evaluation of these approaches, using well-known datasets of the literature and popular evaluation metrics, such as the F1 score; (iii) we provide a series of insights on various keyphrase extraction issues, including alternative approaches and future research directions; (iv) we make the datasets and code used in our experiments public, aiming to further increase the reproducibility of this work and facilitate future research in the field.

Abstract Image

基于深度学习和嵌入的关键词提取方法:文献综述
关键词提取是自然语言处理的一个子任务,指的是自动提取从语义上捕捉文档关键主题和话题的突出术语。早期的文献综述侧重于采用各种统计或基于图的技术的经典方法;这些方法由于无法充分利用文档中的上下文(存在或不存在)而错过了重要的关键词/关键短语,因此获得的 F1 分数较低。深度学习和单词/句子嵌入向量方面的最新进展推动了新方法的发展,这些新方法解决了缺乏上下文的问题,并优于大多数传统方法。综上所述,本综述有四方面的贡献:(i)我们分析了最先进的关键词提取方法,并根据其采用的技术对它们进行了分类;(ii)我们使用文献中的知名数据集和流行的评估指标(如 F1 分数)对这些方法进行了比较评估;(iii)我们就各种关键词提取问题提出了一系列见解,包括替代方法和未来研究方向;(iv)我们公开了实验中使用的数据集和代码,旨在进一步提高这项工作的可重复性,并促进该领域的未来研究。
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来源期刊
Knowledge and Information Systems
Knowledge and Information Systems 工程技术-计算机:人工智能
CiteScore
5.70
自引率
7.40%
发文量
152
审稿时长
7.2 months
期刊介绍: Knowledge and Information Systems (KAIS) provides an international forum for researchers and professionals to share their knowledge and report new advances on all topics related to knowledge systems and advanced information systems. This monthly peer-reviewed archival journal publishes state-of-the-art research reports on emerging topics in KAIS, reviews of important techniques in related areas, and application papers of interest to a general readership.
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