Abstract
This paper presents an unsupervised approach for keyword extraction from a document. Summarizing a research paper is not an easy task, it is time-consuming. For making summarization of documents much easier keywords are used. Keyword helps in summarizing a large amount of textual data. Keyword extraction is a process in which a set of words are selected that gives the context of the whole document. Here, we are proposing a graph-based model approach for extracting keywords from a research paper. The proposing method is a combination of Rapid Automatic Keyword Extraction (RAKE) algorithm and Keyword Extraction using Collective Node Weight (KECNW), in which candidate keywords are extracted using RAKE algorithm and selection of keywords using KECNW model. The final output of this methods are keywords extracted from a document with their rank.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
