Automatic Keyphrase Extraction via Topic Decomposition

By Zhiyuan Liu, Wenyi Huang, Yabin Zheng, Maosong Sun. Conference on Empirical Methods in Natural Language Processing, 2010

390 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7cfff033720ca01f31363f57afaca85e93adf6b7

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Citations in the corpus

Keyphrase Generation for Scientific Document Retrieval (2020, background): “One reason for this lack of evidence may have been their relatively low performance discouraging attempts at using them for indexing (Liu et al., 2010; Hasan and Ng, 2014).”
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): “…therefore introduces competition between candidate keyphrases, and has been shown to significantly outperform KEA (Witten et al., 1999; Frank et al., 1999), a popular supervised baseline that adopts the traditional supervised classification approach (Song et al., 2003; Kelleher and Luz, 2005).”
Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients (2017, methodology): “Topic modeling has been used to extend SingleRank to improve the state-of-the-arts in unsupervised KE [76,77].”
Deep Keyphrase Generation (2017, methodology): “Researchers have tried to use n-grams or noun phrases with certain part-of-speech patterns for identifying potential candidates (Hulth, 2003; Le et al., 2016; Liu et al., 2010; Wang et al., 2016).”
TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction (2013, methodology): “Liu et al. (2010) consider the topics of words using a Latent Dirichlet Allocation model (Blei et al., 2003, LDA).”
Answer-based Adversarial Training for Generating Clarification Questions (2019, background): “Outside reading comprehension questions, Liu et al. (2010) use templated questions to help authors write better related work sections whereas we generate questions to ﬁll information gaps.”