Are Your Keywords Like My Queries? A Corpus-Wide Evaluation of Keyword Extractors with Real Searches (2025.coling-main)
Copied to clipboard
| Challenge: | Keyword Extraction (KE) is essential in Natural Language Processing (NLP) for identifying key terms that represent the main themes of a text. |
| Approach: | They propose to use real query data from Google Trends to evaluate keywords extracted from a text to capture users' top queries. |
| Outcome: | The proposed method can be used with both supervised and unsupervised KE approaches and shows that KeyBERT is the most effective in capturing users’ top queries. |
Similar Papers
A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models (2023.findings-eacl)
Copied to clipboard
| Challenge: | Keyphrase extraction is a key component in Natural Language Processing (NLP) systems for selecting a set of phrases from the document that could summarize the important information discussed in the source document. |
| Approach: | They propose to use supervised and unsupervised keyphrase extraction techniques to investigate the state-of-the-art models for keyphrase extracting. |
| Outcome: | The proposed keyphrase extraction system can significantly accelerate the speed of retrieval and help people get first-hand information from a long document quickly and accurately. |
Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Term weighting schemes are widely used in Natural Language Processing and Information Retrieval. |
| Approach: | They perform an exhaustive and large-scale empirical comparison of term weighting methods in the context of keyword extraction using tf-idf. |
| Outcome: | The proposed methods have advantages over tf-idf, and qualitative differences between them. |
Unsupervised Keyphrase Extraction via Interpretable Neural Networks (2023.findings-eacl)
Copied to clipboard
Rishabh Joshi, Vidhisha Balachandran, Emily Saldanha, Maria Glenski, Svitlana Volkova, Yulia Tsvetkov
| Challenge: | Prior approaches for unsupervised keyphrase extraction relied on heuristic notions of phrase importance via embedding clustering or graph centrality. |
| Approach: | They propose an approach which defines keyphrases as document phrases that are salient for predicting the topic of the document. |
| Outcome: | The proposed method alleviates the need for ad-hoc heuristics and achieves state-of-the-art results in scientific publications and news articles. |
Open-World Evaluation for Retrieving Diverse Perspectives (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing retrieval systems only cover diverse perspectives on 33.74% of the examples . existing systems only focus on relevance to the question, ignoring diversity. |
| Approach: | They build a Benchmark for Retrieval Diversity for Subjective questions (BERDS) based on a question and diverse perspectives associated with the question . they evaluate retrievers paired with a corpus to determine whether each document contains a perspective . |
| Outcome: | The proposed approach improves retrieval diversity on complex questions . existing retrieval systems only cover diverse perspectives on 33.74% of the examples . |
SEKE: Specialised Experts for Keyword Extraction (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Keyword extraction involves identifying the most descriptive words in a document . supervised keyword extraction is based on the mixture of experts (MoE) technique . |
| Approach: | They propose a supervised keyword extraction approach based on the mixture of experts technique . they use a learnable routing sub-network to direct information to specialised experts . |
| Outcome: | The proposed approach is based on the mixture of experts (MoE) technique . experts attend to each token and integrate it with a bidirectional long-term memory network . |
KPEval: Towards Fine-Grained Semantic-Based Keyphrase Evaluation (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing evaluation methods for keyphrase extraction and generation rely on exact matching with human references. |
| Approach: | They propose a framework for evaluation that includes four critical aspects: reference agreement, faithfulness, diversity, utility and semantic-based metrics. |
| Outcome: | The proposed evaluation framework correlates better with human preferences than previously proposed metrics. |
Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings (N18-2)
Copied to clipboard
| Challenge: | Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. |
| Approach: | They propose an unsupervised technique that leverages phrase embeddings for ranking keyphrases extracted from scientific articles using theme-weighted PageRank. |
| Outcome: | The proposed method performs better on benchmark datasets than other methods and is of high quality. |
MAKED: Multi-lingual Automatic Keyword Extraction Dataset (2022.lrec-1)
Copied to clipboard
| Challenge: | a large dataset of news articles spanning 20 languages is lacking for keyword extraction. |
| Approach: | They propose a large-scale multi-lingual keyword extraction dataset for 11 of 20 languages . authors believe it will help advance the field of automatic keyword extraction . |
| Outcome: | The proposed dataset is the first for 11 of 20 languages and is based on 540K+ news articles from the BBC News network. |
SimCKP: Simple Contrastive Learning of Keyphrase Representations (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models for keyphrase generation and keyphrase extraction use a token level to generate keyphrases that do not appear in a document. |
| Approach: | They propose a simple contrastive learning framework that generates keyphrases that do not appear in a document and a reranker that adapts the scores for each generated phrase. |
| Outcome: | The proposed model outperforms the state-of-the-art models on multiple benchmark datasets. |
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions (2021.emnlp-main)
Copied to clipboard
| Challenge: | Keyword or keyphrase extraction is to identify words or phrases presenting the main topics of a document. |
| Approach: | They propose a hybrid attention model to identify keyphrases from a document in an unsupervised manner. |
| Outcome: | The proposed model is effective and robust on long and short documents. |