Unsupervised keyword extraction using the GoW model and centrality scores

Batziou, Elissavet; Gialampoukidis, Ilias; Vrochidis, Stefanos; Kompatsiaris, Ioannis

doi:http://dx.doi.org/10.1007/978-3-319-70284-1

Unsupervised keyword extraction using the GoW model and centrality scores

Citation

Batziou E, Gialampoukidis I, Vrochidis S, Kompatsiaris I. Unsupervised keyword extraction using the GoW model and centrality scores. In: Kompatsiaris I, Cave J, Satsiou A, Carle G, Passani A, Kontopoulos E, Diplaris S, McMillan D. Internet Science. 4th International Conference, INSCI 2017 Proceedings. 2017 Nov 22-24; Thessaloniki, Greece. [Cham]: Springer, 2017. (LNCS; no. 10673). DOI: 10.1007/978-3-319-70284-1

Abstract

Nowadays, a large amount of text documents are produced on a daily basis, so we need e cient and e ective access to their con- tent. News articles, blogs and technical reports are often lengthy, so the reader needs a quick overview of the underlying content. To that end we present graph-based models for keyword extraction, in order to compare the Bag of Words model with the Graph of Words model in the key- word extraction problem. We compare their performance in two publicly available datasets using the evaluation measures Precision@10, mean Av- erage Precision and Jaccard coe cient. The methods we have selected for comparison are grouped into two main categories. On the one hand, centrality measures on the formulated Graph-of-Words (GoW) are able to rank all words in a document from the most central to the less central, according to their score in the GoW representation. On the other hand, community detection algorithms on the GoW provide the largest commu- nity that contains the key nodes (words) in the GoW. We selected these methods as the most prominent methods to identify central nodes in a GoW model. We conclude that term-frequency scores (BoW model) are useful only in the case of less structured text, while in more structured text documents, the order of words plays a key role and graph-based models are superior to the term-frequency scores per document.