Automatic Keyword Extraction from Individual Documents

By Stuart J. Rose, Dave W. Engel, Nick Cramer, Wendy Cowley. 2010

1,233 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7e0006dc1972e276805d1bd6c5dc2813c7a6d824

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Molecular Biology

Citations in the corpus

Towards Controllable Story Generation (2018, methodology): “We adapt the RAKE algorithm (Rose et al., 2010) for keyword extraction, which builds document graphs and weights the importance of each word combining several word-level and graph-level criteria.”
Controllable Story Generation with External Knowledge Using Large-Scale Language Models (2020, methodology): “Following our hypothesis, we first extract keywords Ki from si using RAKE (Rose et al., 2010) and then match Ki with all knowledge triples in G.”
Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models (2020, methodology): “Figure 4: We extract phrases from the main themes of recalled (left) and imagined (right) stories, using RAKE (Rose et al., 2010); size of words corresponds to frequency in corpus, and color is only for readability.”
CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling (2018, methodology): “In this variant, we use Rake (Rose et al. 2010) to extract keywords and keep them ﬁxed during sampling.”
(Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas (2019, methodology): “We For the keyword selection, we use RAKE al-gorithm (Rose et al., 2010) to extract keywords and rank them for each sentence by the output score.”
Plan-And-Write: Towards Better Automatic Storytelling (2018, methodology): “algorithm (Rose et al. 2010), which combines several word frequency based and graph-based metrics to weight the importance of the words.”
Strategies for Structuring Story Generation (2019, methodology): “(2019) use RAKE (Rose et al., 2010) to extract storylines, and condition upon the storyline to write the story using dynamic and static schemas that govern if the storyline can change.”
Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level (2015, methodology): “The commercially available text mining program IN-SPIRE (Pacific Northwest National Laboratories, Richland, Washington) [76] was used for content-based clustering of citations (Fig 1).”
AmbiPun: Generating Humorous Puns with Ambiguous Context (2022, methodology): “Method 1: Extractive (TF-IDF) For each related word, we retrieve sentences from the One Billion Word dataset containing that word and then extract keywords using RAKE (Rose et al., 2010) from the retrieved sentences.”
GenAug: Data Augmentation for Finetuning Text Generators (2020, neutral): “We use RAKE (Rose et al., 2010) for keyword extraction.”
Progressive Generation of Long Text with Pretrained Language Models (2021, methodology): “Following (Yao et al., 2019), the keywords are extracted with the RAKE algo-rithm (Rose et al., 2010) for training; SRL follows the recent work (Fan et al., 2019) by ﬁrst generating a sequence of predicates and arguments and then producing the full text conditionally.”
Overview of Touché 2022: Argument Retrieval (2022, neutral): “BM25 and Dirichlet-based document relevance and sentence relevance were used for retrieval along with Rapid Automatic Keyword Extraction (RAKE) [74] query expansion.”
A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation (2020, methodology): “During training, one keyword is extracted from each sentence with RAKE algorithm (Rose et al., 2010).”