| Challenge: | Existing studies on keyphrase extraction neglect human reading behavior during keyphrase annotating. |
| Approach: | They propose to integrate human attention into keyphrase extraction models by an attention mechanism and combine it with neural network models. |
| Outcome: | The proposed models improve on two Twitter datasets. |
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. |
Attention-Seeker: Dynamic Self-Attention Scoring for Unsupervised Keyphrase Extraction (2025.coling-main)
Copied to clipboard
| Challenge: | Unsupervised keyphrase extraction methods require large amounts of labeled data and are often domainspecific, limiting their practical applicability. |
| Approach: | They propose an unsupervised keyphrase extraction method that leverages self-attention maps from a Large Language Model to estimate the importance of candidate phrases. |
| Outcome: | The proposed method outperforms baseline models on four datasets and is highly efficient on three of four dataset. |
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. |
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. |
Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts (N18-1)
Copied to clipboard
| Challenge: | Existing keyphrase extraction methods suffer from data sparsity problem when conducted on short and informal texts. |
| Approach: | They propose a neural keyphrase extraction framework for microblog posts that takes conversation context into account and uses four types of neural encoders to represent conversation context. |
| Outcome: | The proposed framework outperforms state-of-the-art keyphrase extraction methods on Twitter and Weibo datasets. |
Unsupervised Keyphrase Extraction by Learning Neural Keyphrase Set Function (2023.findings-acl)
Copied to clipboard
| Challenge: | Unsupervised keyphrase extraction is a task of extracting a keyphrase set that provides readers with highlevel information about the key ideas or important topics described in the document. |
| Approach: | They propose an unsupervised keyphrase extraction task that is a document-set matching problem instead of modeling the relevance between an individual phrase and the document. |
| Outcome: | The proposed model outperforms the state-of-the-art unsupervised keyphrase extraction baselines by a large margin. |
Topic-Aware Neural Keyphrase Generation for Social Media Language (P19-1)
Copied to clipboard
| Challenge: | Existing methods to extract words from source posts to form keyphrases do not exploit latent topics. |
| Approach: | They propose a sequence-to-sequence-based neural keyphrase generation framework . it allows absent keyphrases to be created, and it allows joint modeling of latent topic representations . |
| Outcome: | The proposed model outperforms extraction and generation models without exploiting latent topics. |
Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies focus on text modeling, ignoring the rich features embedded in the matching images. |
| Approach: | They propose a novel multi-modal multi-head attention model to capture cross-media interactions and image wordings to bridge the two modalities. |
| Outcome: | The proposed model outperforms the current state of the art based on text modeling and image matching . |
SAMRank: Unsupervised Keyphrase Extraction using Self-Attention Map in BERT and GPT-2 (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for keyphrase extraction use contextualized embeddings to capture semantic relevance between words, sentences, and documents. |
| Approach: | They propose an unsupervised keyphrase extraction approach that uses only a self-attention map in a pre-trained language model to determine the importance of phrases. |
| Outcome: | The proposed approach outperforms embedding-based models on three keyphrase extraction datasets. |
Match More, Extract Better! Hybrid Matching Model for Open Domain Web Keyphrase Extraction (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing models for keyphrase extraction use noisy information to filter the salient phrases from the document. |
| Approach: | They propose a hybrid matching model that combines representation-focused and interaction-based matching modules into a unified framework for improving keyphrase extraction. |
| Outcome: | The proposed model outperforms state-of-the-art keyphrase extraction models on the OpenKP dataset. |