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HAKE: an Unsupervised Approach to Automatic Keyphrase Extraction for Multiple Domains

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Abstract

Keyphrases capture the main content of a free text document. The task of automatic keyphrase extraction (AKPE) plays a significant role in retrieving and summarizing valuable information from several documents with different domains. Various techniques have been proposed for this task. However, supervised AKPE requires large annotated data and depends on the tested domain. An alternative solution is to consider a new independent domain method that can be applied to several domains (such as medical, social). In this paper, we tackle keyphrase extraction from single documents with HAKE, a novel unsupervised method that takes full advantage of mining linguistic, statistical, structural, and semantic text features simultaneously to select the most relevant keyphrases in a text. HAKE achieves higher F-scores than the unsupervised state-of-the-art systems on standard datasets and is suitable for real-time processing of large amounts of Web and text data across different domains. With HAKE, we also explicitly increase coverage and diversity among the selected keyphrases by introducing a novel technique (based on a parse tree approach, part of speech tagging, and filtering) for candidate keyphrase identification and extraction. This technique allows us to generate a comprehensive and meaningful list of candidate keyphrases and reduce the candidate set’s size without increasing the computational complexity. HAKE’s effectiveness is compared to twelve state-of-the-art and recent unsupervised approaches, as well as to some other supervised approaches. Experimental analysis is conducted to validate the proposed method using five of the top available benchmark corpora from different domains and shows that HAKE significantly outperforms both the existing unsupervised and supervised methods. Our method does not require training on a particular set of documents, nor does it depend on external corpora, dictionaries, domain, or text size. Our experiments confirm that HAKE’s candidate selection model and its ranking model are effective.

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  1. LIASSE Lab, National School of Applied Sciences (ENSA), Sidi Mohamed Ben Abdellah University, B.P. 72, Route d’imouzer, Fez, Morocco

    Zakariae Alami Merrouni & Bouchra Frikh

  2. Mathematical Modeling & Computer Laboratory (LM2I), National Higher School of Arts and Crafts (ENSAM), Moulay Ismail University (UMI), Marjane II, B.P. 4024, Meknes, Morocco

    Brahim Ouhbi

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Merrouni, Z.A., Frikh, B. & Ouhbi, B. HAKE: an Unsupervised Approach to Automatic Keyphrase Extraction for Multiple Domains. Cogn Comput 14, 852–874 (2022). https://doi.org/10.1007/s12559-021-09979-7

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