A Text Feature Based Automatic Keyword Extraction Method for Single Documents
In this work, we propose a lightweight approach for keyword extraction and ranking based on an unsupervised methodology to select the most important keywords of a single document. To understand the merits of our proposal, we compare it against RAKE, TextRank and SingleRank methods (three well-known unsupervised approaches) and the baseline TF.IDF, over four different collections to illustrate the generality of our approach. The experimental results suggest that extracting keywords from documents using our method results in a superior effectiveness when compared to similar approaches.
KeywordsKeyword extraction Information extraction Feature extraction
This work is partially funded by the ERDF through the COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT as part of project UID/EEA/50014/2013 and of project UID/MAT/00212/2013. It was also financed by MIC SCOPE (171507010) and by Project “TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020” which is financed by the NORTE 2020, under the PORTUGAL 2020, and through the ERDF.
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