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Computer Science > Computation and Language
Title: Keyphrase Generation: A Text Summarization Struggle
(Submitted on 29 Mar 2019 (v1), last revised 3 Apr 2019 (this version, v2))
Abstract: Authors' keyphrases assigned to scientific articles are essential for recognizing content and topic aspects. Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text. In this paper, we explore the possibility of considering the keyphrase string as an abstractive summary of the title and the abstract. First, we collect, process and release a large dataset of scientific paper metadata that contains 2.2 million records. Then we experiment with popular text summarization neural architectures. Despite using advanced deep learning models, large quantities of data and many days of computation, our systematic evaluation on four test datasets reveals that the explored text summarization methods could not produce better keyphrases than the simpler unsupervised methods, or the existing supervised ones.
Submission history
From: Erion Çano [view email][v1] Fri, 29 Mar 2019 22:43:26 GMT (134kb)
[v2] Wed, 3 Apr 2019 19:54:28 GMT (31kb)
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