KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Massachussets Amherst（马萨诸塞大学阿默斯特分校）
英文摘要：
We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework for topical keyphrase generation and ranking. By shifting from the unigram-centric traditional methods of unsupervised keyphrase extraction to a phrase-centric approach, we are able to directly compare and rank phrases of different lengths. We construct a topical keyphrase ranking function which implements the four criteria that represent high quality topical keyphrases (coverage, purity, phraseness, and completeness). The effectiveness of our approach is demonstrated on two collections of content-representative titles in the domains of Computer Science and Physics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
