Research Area:  Machine Learning
Keyphrases that efficiently summarize a document’s content are used in various document processing and retrieval tasks. Current state-of-the-art techniques for keyphrase extraction operate at a phrase-level and involve scoring candidate phrases based on features of their component words.In this paper, we learn keyphrase taggers for research papers using token-based features incorporating linguistic, surface-form, and document-structure information through sequence labeling. We experimentally illustrate that using within document features alone, our tagger trained with ConditionalRandom Fields performs on-par with existing state-of-the-art systems that rely on information from Wikipedia and citation networks. In addition, we are also able to harness recent work on feature labeling to seamlessly incorporate expert knowledge and predictions from existing systems to enhance the extraction performance further. We highlight the modeling advantages of our keyphrase taggers and show significant performance improvements on two recently-compiled datasets of keyphrases from Computer Science research papers.
Keywords:  
Expert Knowledge
Keyphrase Extraction
conditional random fields
feature labeling
Machine Learning
Deep Learning
Author(s) Name:  Sujatha Das Gollapalli, Xiao-li Li, Peng Yang
Journal name:  
Conferrence name:   Vol. 31 No. 1 (2017): Thirty-First AAAI Conference on Artificial Intelligence
Publisher name:  Association for the Advancement of Artificial Intelligence
DOI:  10.1609/aaai.v31i1.10986
Volume Information:  
Paper Link:   https://ojs.aaai.org/index.php/AAAI/article/view/10986