Keyphrase Extraction
47 papers with code • 6 benchmarks • 6 datasets
A classic task to extract salient phrases that best summarize a document, which essentially has two stages: candidate generation and keyphrase ranking.
Benchmarks
These leaderboards are used to track progress in Keyphrase Extraction
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KP20k
SemEval-2017 Task-10
Most implemented papers
Persian Keyphrase Generation Using Sequence-to-Sequence Models
Keyphrases are a very short summary of an input text and provide the main subjects discussed in the text.
KeyGames: A Game Theoretic Approach to Automatic Keyphrase Extraction
In this paper, we introduce two advancements in the automatic keyphrase extraction (AKE) space - KeyGames and pke+.
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.
UIUC_BioNLP at SemEval-2021 Task 11: A Cascade of Neural Models for Structuring Scholarly NLP Contributions
We propose a cascade of neural models that performs sentence classification, phrase recognition, and triple extraction to automatically structure the scholarly contributions of NLP publications.
Wizard of Search Engine: Access to Information Through Conversations with Search Engines
(2) We release a benchmark dataset, called wizard of search engine (WISE), which allows for comprehensive and in-depth research on all aspects of CIS.
Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context
xnliang98/uke_ccrank
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EMNLP 2021
In terms of the local view, we first build a graph structure based on the document where phrases are regarded as vertices and the edges are similarities between vertices.
Multi-Document Keyphrase Extraction: Dataset, Baselines and Review
Keyphrase extraction has been extensively researched within the single-document setting, with an abundance of methods, datasets and applications.
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction
linhanz/mderank
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Findings (ACL) 2022
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.
Enhancing Keyphrase Extraction from Academic Articles with their Reference Information
chengzhizhang/keyphrase_extraction
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This indicates the usefulness of reference information on keyphrase extraction of academic papers and provides a new idea for the following research on automatic keyphrase extraction.
Learning Rich Representation of Keyphrases from Text
bloomberg/kbir_keybart
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Findings (NAACL) 2022
In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (upto 8. 16 points in F1) over SOTA, when the LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction.
