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Keyphrases are widely used as a brief summary of documents. Since manual assignment is time-consuming, various unsupervised ranking methods based on importance scores are proposed...
This paper connects two research areas: automatic tagging on the web and statistical keyphrase extraction. First, we analyze the quality of tags in a collaboratively created folks...
Existing graph-based ranking methods for keyphrase extraction compute a single importance score for each word via a single random walk. Motivated by the fact that both documents a...
Zhiyuan Liu, Wenyi Huang, Yabin Zheng, Maosong Sun
In this paper we present a keyphrase extraction system that can extract potential phrases from a single document in an unsupervised, domain-independent way. We extract word n-grams...
Nirmala Pudota, Antonina Dattolo, Andrea Baruzzo, ...
Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic ...
Previous methods usually conduct the keyphrase extraction task for single documents separately without interactions for each document, under the assumption that the documents are ...
The automatic generation of back-of-the book indexes seems to be out of sight of the Information Retrieval and Natural Language Processing communities, although the increasingly la...
Abstract. The ability to discover the topic of a large set of text documents using relevant keyphrases is usually regarded as a very tedious task if done by hand. Automatic keyphra...
Khaled M. Hammouda, Diego N. Matute, Mohamed S. Ka...
We propose a new method that enhances automatic keyphrase extraction by using semantic information on terms and phrases gleaned from a domain-specific thesaurus. We evaluate the r...
Prospective readers can quickly determine whether a document is relevant to their information need if the significant phrases (or keyphrases) in this document are provided. Althou...