Keyword Extraction
25 papers with code • 3 benchmarks • 5 datasets
Keyword extraction is tasked with the automatic identification of terms that best describe the subject of a document (Source: Wikipedia).
Datasets
Most implemented papers
Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization
Component (a) directly generates a matching score of a candidate document for a query.
Outline to Story: Fine-grained Controllable Story Generation from Cascaded Events
Our paper is among the first ones by our knowledge to propose a model and to create datasets for the task of "outline to story".
Extending Neural Keyword Extraction with TF-IDF tagset matching
Keyword extraction is the task of identifying words (or multi-word expressions) that best describe a given document and serve in news portals to link articles of similar topics.
ELSKE: Efficient Large-Scale Keyphrase Extraction
Keyphrase extraction methods can provide insights into large collections of documents such as social media posts.
FRAKE: Fusional Real-time Automatic Keyword Extraction
For evaluating the proposed method, seven datasets were used: Semeval2010, SemEval2017, Inspec, fao30, Thesis100, pak2018, and Wikinews, with results reported as Precision, Recall, and F- measure.
Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction
Term weighting schemes are widely used in Natural Language Processing and Information Retrieval.
DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature
In this work, we present to the NLP community, and to the wider research community as a whole, an application for the diachronic analysis of research corpora.
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction
In this work, we propose a novel unsupervised embedding-based KPE approach, Masked Document Embedding Rank (MDERank), to address this problem by leveraging a mask strategy and ranking candidates by the similarity between embeddings of the source document and the masked document.
Study of keyword extraction techniques for Electric Double Layer Capacitor domain using text similarity indexes: An experimental analysis
This paper presents an experimental analysis of similarity scores of keywords generated by different supervised and unsupervised automated keyword extraction algorithms with expert provided keywords from the Electric Double Layer Capacitor (EDLC) domain.
SiDi KWS: A Large-Scale Multilingual Dataset for Keyword Spotting
Keyword spotting (KWS) has become a hot topic in speech processing due to the rise of commercial applications based on voice command detection, such as voice assistants.