Keyword Extraction Using Graph Centrality and WordNet Chhavi Sharma, Minni Jain and Ayush Aggarwal 1 Introduction Keywords are a short representation of a document which summarizes the important content of the document into certain selective words which are either picked from the document or constructed using the underlying sense of the document. Keyword extraction deals with automatically picking up the underlying text from the document which can best describe the senses of it. Keyword extraction is a very important field of study for NLP purposes because it serves great many functionality. The amount of text being produced daily is increas- ing exponentially and it becomes impossible for the reader to sift through it, and hence, keyword presents an alternative of understanding for the user to determine whether the document deserves the time or not. Keywords are also used in informa- tion retrieval systems [1] such as search engines to better index the documents and provide document management and categorization. A lot of research has been done in the field of keyword extraction from supervised to graph-based approach, but not much work has been done exploring the semantic relatedness of the term and ranking them on the language tree to determine the central words of the documents. In this paper, we present an unsupervised learning approach to extract keywords from the document which are based on the semantic strength of words in a particular document. We use WordNet [2] as our knowledge base to C. Sharma · M. Jain (B ) · A. Aggarwal Delhi Technological University, Shahbad Daulatpur, Bawana Road, New Delhi 110042, Delhi, India e-mail: minnijain91@gmail.com; minnijain@dtu.ac.in C. Sharma e-mail: sharma.chhavi96@gmail.com A. Aggarwal e-mail: aggarwal96ayush@gmail.com © Springer Nature Singapore Pte Ltd. 2018 S. Chakraverty et al. (eds.), Towards Extensible and Adaptable Methods in Computing, https://doi.org/10.1007/978-981-13-2348-5_27 363