TOP-Rank: A TopicalPostionRank for Extraction and Classification of Keyphrases in Text

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. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The paper presents TOP-Rank, an approach for keyphrase extraction and keyphrase classification that expands the method based on the position of keyphrases with topical ranking, classifying keyphrases into process, material, and task categories. Achieving an F1-score of 0.73 for keyphrase classification, TOP-Rank significantly improves upon existing methods.