TOP-Rank: A TopicalPostionRank for Extraction and Classification of Keyphrases in Text. (January 2021) - Awan, Mubashar Nazar; Beg, Mirza Omer
Highlights: We propose TOP-Rank which incorporates both topical and positional information to rank phrases. Classification of phrases in keyphrases and non-keyphrases by identifying topical significance using similarity measures. Ranking of keyphrases using intra-cluster and inter-cluster ranking to identify the top most keyphrase from top most cluster. Comparison of the similarity measures for keyphrase extraction. Improving classification of keyphrases as Processes, Materials and Tasks. Abstract: Keyphrase extraction is the task of extracting the most important phrases from a document. Automatic keyphrase extraction attempts to itemize a document content as metainformation and facilitate efficient information retrieval. In this paper we propose TOP-Rank, an approach for keyphrase extraction and keyphrase classification. For keyphrase extraction, we build an approach based on the position of keyphrases in the document and expand it with topical ranking of keyphrases. In particular, keyphrase extraction technique analyzes the documents and extracts keyphrases from the document by giving a higher rank to topical phrases. After keyphrase extraction, we classify keyphrases as process, material and task . Our evaluation on diverse datasets shows that TOP-Rank achieves F1-score of 0.73 for keyphrase classification improving upon state-of-the-art methods by a huge margin.