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WIREs Data Mining Knowl Discov
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A review of keyphrase extraction

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Abstract Keyphrase extraction is a textual information processing task concerned with the automatic extraction of representative and characteristic phrases from a document that express all the key aspects of its content. Keyphrases constitute a succinct conceptual summary of a document, which is very useful in digital information management systems for semantic indexing, faceted search, document clustering and classification. This article introduces keyphrase extraction, provides a well‐structured review of the existing work, offers interesting insights on the different evaluation approaches, highlights open issues and presents a comparative experimental study of popular unsupervised techniques on five datasets. This article is categorized under: Ensemble Methods > Web Mining Ensemble Methods > Text Mining
Presentation structure of the unsupervised keyphrase extraction methods
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Differences between the F1‐scores based on the manual evaluation and the F1‐scores based on the exact (solid line), partial (dashed line) and average (dotted line) evaluation approaches for the 50 manually evaluated documents given on the x axis
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Distribution of differences between the F1‐scores based on the manual evaluation and the F1‐scores based on the exact (Exact), partial (Partial) and average (Average) evaluation approaches for the 50 manually evaluated documents given on the x axis
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Box plots of the F1@10 scores obtained via manual evaluation, exact matching, partial matching and taking the average of exact and partial matching
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Popularity of evaluation approaches. ACEK, MRR, MAP and B‐pref are used along with the exact match evaluation approach
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Presentation structure of supervised keyphrase extraction methods
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Algorithmic Development > Text Mining
Algorithmic Development > Web Mining

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