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).
Benchmarks
These leaderboards are used to track progress in Keyword Extraction
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Datasets
MPQA Opinion Corpus
SemEval-2017 Task-10
Latest papers
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.
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.
Extending Neural Keyword Extraction with TF-IDF tagset matching
bkolosk1/extending-neural-keyword-extraction-with-tf-idf-tagset-matching
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EACL (Hackashop) 2021
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.
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".
Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization
bionlproc/text-summ-for-doc-retrieval
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Findings of the Association for Computational Linguistics 2020
Component (a) directly generates a matching score of a candidate document for a query.
Keywords lie far from the mean of all words in local vector space
Keyword extraction is an important document process that aims at finding a small set of terms that concisely describe a document's topics.
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
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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.
Complex Network based Supervised Keyword Extractor
This shows that the proposed method is independent of the domain, collection, and language of the training corpora.
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.
