Abstract
We live in a world where information is available, in all areas of human activity, increasingly in digital text documents. It is necessary to explore the knowledge implied in these documents considering its fast-growing availability. The use of keywords provides for a more effective search for a document of interest, as keywords highlight a document's primary concept and, therefore, allow the researcher's interest to be readily aligned with that text. In this article, an unsupervised keyword extraction approach is proposed. The proposed approach retrofitted the concept of n-grams with state-of-the-art words and document embeddings. The approach simultaneously proposed a new method to compose document vectors using important word vectors and their idf-scores. Here we use higher-order word n-grams to improve various unigram embeddings and introduce a novel task to produce document embedding for document representation. The performance of the proposed embeddings is evaluated using four different datasets. The combination of higher-order word n-grams retrofitted Glove, and document embedding is the best embedding to be used for extracting key phrases. The bi-gram retrofitted embedding improves the results significantly over the baseline approaches.