Single Document Keyphrase Extraction Using Neighborhood Knowledge

By Xiaojun Wan, Jianguo Xiao. AAAI Conference on Artificial Intelligence, 2008

537 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8a99634e0b418ee61c9bd81f61d334b80486dc53

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Citations in the corpus

Neural Models for Key Phrase Extraction and Question Generation (2018, background): “Mihalcea and Tarau (2004); Wan and Xiao (2008); Le et al. (2016) scored key phrases using PageRank .”
TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction (2013, methodology): “Wan and Xiao (2008) use a small number of nearest neighbor documents to compute more accurate word co-occurrences and reinforce edge weights in the word graph.”
Clustering to Find Exemplar Terms for Keyphrase Extraction (2009, methodology): “Wan and Xiao (Wan and Xiao, 2008b) use a small number of nearest neighbor documents to provide more knowledge to improve graph-based keyphrase extraction algorithm for single document.”
Deep Keyphrase Generation (2017, methodology): “We use the popular news article dataset DUC-2001 (Wan and Xiao, 2008) for analysis.”
Automatic Keyphrase Extraction via Topic Decomposition (2010, methodology): “One dataset was built by Wan and Xiao 2 which was used in (Wan and Xiao, 2008b).”
PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents (2017, methodology): “As already mentioned, prior works on keyphrase extraction report results also in terms of precision (P), recall (R), and F1-score (F1) (Hulth, 2003; Hasan and Ng, 2010; Liu et al., 2010; Wan and Xiao, 2008).”
Automatic Keyphrase Extraction: A Survey of the State of the Art (2014, methodology): “(Mihalcea and Tarau, 2004; Wan and Xiao, 2008a; Wan and Xiao, 2008b; Bougouin et al., 2013).”