TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction
Code (0)
등록된 구현이 없습니다.
Tasks
Document SummarizationInformation RetrievalKeyphrase ExtractionLanguage ModellingSimilar Papers 제목 키워드 기반
KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles
We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework for topical keyphrase generation and ranking. By shifting from the unigram-centric traditional methods of unsupervised keyphrase extraction to a …
Keyphrase ExtractionKeyphrase GenerationUnsupervised Keyphrase Extraction with Multipartite Graphs
We propose an unsupervised keyphrase extraction model that encodes topical information within a multipartite graph structure. Our model represents keyphrase candidates and topics in a single graph and exploits their mutu…
Keyphrase ExtractionTopic Aware Contextualized Embeddings for High Quality Phrase Extraction
Keyphrase extraction from a given document is the task of automatically extracting salient phrases that best describe the document. This paper proposes a novel unsupervised graph-based ranking method to extract high-qual…
Keyphrase ExtractionVocal Bursts Intensity PredictionSalience Rank: Efficient Keyphrase Extraction with Topic Modeling
Topical PageRank (TPR) uses latent topic distribution inferred by Latent Dirichlet Allocation (LDA) to perform ranking of noun phrases extracted from documents. The ranking procedure consists of running PageRank K times,…
Keyphrase ExtractionPart-Of-Speech TaggingSpecificityKeyphrase Extraction Using Neighborhood Knowledge Based on Word Embeddings
Keyphrase extraction is the task of finding several interesting phrases in a text document, which provide a list of the main topics within the document. Most existing graph-based models use co-occurrence links as cohesio…
Keyphrase ExtractionWord Embeddings