Despite the advances, there is a clear lack of multilingual online tools to automatically extract keywords from single documents.
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The need to automate this task so that texts can be processed in a timely and adequate manner has led to the emergence of automatic keyword extraction tools. RationaleĮxtracting keywords from texts has become a challenge for individuals and organizations as the information grows in complexity and size. Main Featuresįor Benchmark results check out our paper published on Information Science Journal (see the references section). In addition to the python package here described, we also make available a demo, an API and a mobile app. Experimental results carried out on top of twenty datasets (see Benchmark section below) show that our methods significantly outperform state-of-the-art methods under a number of collections of different sizes, languages or domains. To demonstrate the merits and the significance of our proposal, we compare it against ten state-of-the-art unsupervised approaches (TF.IDF, KP-Miner, RAKE, TextRank, SingleRank, ExpandRank, TopicRank, TopicalPageRank, PositionRank and MultipartiteRank), and one supervised method (KEA). Our system does not need to be trained on a particular set of documents, neither it depends on dictionaries, external-corpus, size of the text, language or domain.
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YAKE! is a light-weight unsupervised automatic keyword extraction method which rests on text statistical features extracted from single documents to select the most important keywords of a text. Unsupervised Approach for Automatic Keyword Extraction using Text Features.