Keyword Extraction Edit Task
Natural Language Processing
About
Keyword extraction is tasked with the automatic identification of terms that best describe the subject of a document (Source: Wikipedia).
Benchmarks
Datasets
Greatest papers with code
RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation
Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems.
Efficient Generation and Processing of Word Co-occurrence Networks Using corpus2graph
Corpus2graph is an open-source NLP-application-oriented tool that generates a word co-occurrence network from a large corpus.
Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization
We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research.
DOCUMENT SUMMARIZATION EXTRACTIVE TEXT SUMMARIZATION INFORMATION RETRIEVAL KEYWORD EXTRACTION SENTENCE COMPRESSION
sCAKE: Semantic Connectivity Aware Keyword Extraction
Combination of the proposed graph construction and scoring methods leads to a novel, parameterless keyword extraction method (sCAKE) based on semantic connectivity of words in the document.
Complex Network based Supervised Keyword Extractor
This shows that the proposed method is independent of the domain, collection, and language of the training corpora.
ELSKE: Efficient Large-Scale Keyphrase Extraction
• johpro/elske
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Keyphrase extraction methods can provide insights into large collections of documents such as social media posts.
INFORMATION RETRIEVAL KEYWORD EXTRACTION MULTI-DOCUMENT SUMMARIZATION
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".
TNT-KID: Transformer-based Neural Tagger for Keyword Identification
With growing amounts of available textual data, development of algorithms capable of automatic analysis, categorization and summarization of these data has become a necessity.
Semantic Sensitive TF-IDF to Determine Word Relevance in Documents
• vfcarida/Semantic-Sensitive-TF-IDF-to-Determine-Word-Relevance-in-Documents
•
Keyword extraction has received an increasing attention as an important research topic which can lead to have advancements in diverse applications such as document context categorization, text indexing and document classification.