Papers with keyphrase extraction
Match More, Extract Better! Hybrid Matching Model for Open Domain Web Keyphrase Extraction (2024.findings-acl)
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| 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. |
A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents (2020.coling-main)
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| Challenge: | Existing methods for keyphrase extraction are limited by the number of annotated documents. |
| Approach: | They propose a joint learning approach that uses the idea of self-distillation to extract keyphrases from unlabeled articles. |
| Outcome: | The proposed approach outperforms baseline models on two public benchmarks: Inspec and SemEval-2017. |
Learning Rich Representation of Keyphrases from Text (2022.findings-naacl)
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| Challenge: | Prior work has referred to extractive (part of document) or abstractive (not part of document). |
| Approach: | They propose to use a new pre-training objective to introduce keyphrases into transformer language models in discriminative and generative settings. |
| Outcome: | The proposed model improves performance in discriminative and generative settings and also improves on named entity recognition, question answering, relation extraction and abstractive summarization tasks. |
Clustering-based Sampling for Few-Shot Cross-Domain Keyphrase Extraction (2024.findings-eacl)
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| Challenge: | Scientific domain-specific pre-training has led to state-of-the-art keyphrase extraction performance with a majority of benchmarks being within the domain. |
| Approach: | They propose to leverage topic information available in the data to build a clustering-based sampling approach that facilitates selecting a few samples to label from the target domain . |
| Outcome: | The proposed approach leads to 26.35 points in performance when compared to selecting few-shot samples uniformly at random. |
Improving Absent Keyphrase Generation with Diversity Heads (2024.findings-naacl)
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| Challenge: | Existing approaches to generating keyphrases for a given text are limited to extracting only the keyphrase that is directly seen in the document. |
| Approach: | They propose to treat present keyphrase extraction as a sequence labeling problem and treat absent keyphrases together in a text-to-text generation framework during training. |
| Outcome: | The proposed model improves on the state-of-the-art for present keyphrase extraction and five datasets for absent keyphrase generation among the six English datasets. |
KPEval: Towards Fine-Grained Semantic-Based Keyphrase Evaluation (2024.findings-acl)
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| 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. |
Exploiting Position and Contextual Word Embeddings for Keyphrase Extraction from Scientific Papers (2021.eacl-main)
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| Challenge: | Existing methods for keyphrase extraction are either supervised or unsupervised. |
| Approach: | They propose an unsupervised algorithm that exploits contextual word embeddings and positional information to create a biased PageRank. |
| Outcome: | The proposed algorithm outperforms previous approaches and strong baselines on five benchmark datasets. |
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions (2021.emnlp-main)
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| 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. |
A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models (2023.findings-eacl)
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| 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. |
Glocal: Incorporating Global Information in Local Convolution for Keyphrase Extraction (N19-1)
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| Challenge: | Graph Convolutional Networks (GCNs) model nodes’ local pairwise importance but lack the capability to model global relative importance in tasks where global ranking is a key component for the task. |
| Approach: | They propose to incorporate global relative importance information into the GCN family of models by using scaled node weights. |
| Outcome: | The proposed method improves keyphrase extraction by 2% and improves the baseline by 5%. |
SimCKP: Simple Contrastive Learning of Keyphrase Representations (2023.findings-emnlp)
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| 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. |
Importance Estimation from Multiple Perspectives for Keyphrase Extraction (2021.emnlp-main)
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| Challenge: | Existing keyphrase extraction methods focus on the part of phrase that is important . experimental results show that KIEMP outperforms existing keyphrase extracting methods . |
| Approach: | They propose to estimate the importance of keyphrase from multiple perspectives using a chunking module, ranking module and matching module. |
| Outcome: | The proposed method outperforms the state-of-the-art keyphrase extraction methods on six benchmark datasets. |
Rethinking Schema Linking: A Context-Aware Bidirectional Retrieval Approach for Text-to-SQL (2026.findings-eacl)
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| Challenge: | Recent methods focus on improving SQL generation but neglect retrieval of relevant schema elements. |
| Approach: | They propose a context-aware bidirectional schema retrieval framework that treats schema linking as a standalone problem. |
| Outcome: | The proposed framework improves schema recall while reducing false positives. |
Hyperbolic Relevance Matching for Neural Keyphrase Extraction (2022.naacl-main)
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| Challenge: | Keyphrase extraction is a fundamental task in natural language processing that aims to extract a set of phrases with important information from a source document. |
| Approach: | They propose a hyperbolic matching model to explore keyphrase extraction in hyperbolical space using word embeddings from RoBERTa to capture hierarchical syntactic and semantic structures. |
| Outcome: | The proposed model outperforms the state-of-the-art models on six benchmark datasets and outperformed previous models. |
SaSAKE: Syntax and Semantics Aware Keyphrase Extraction from Research Papers (2020.coling-main)
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| Challenge: | Syntax and semantics Aware Keyphrase Extraction (SaSAKE) is a novel method for extracting keyphrases from research papers. |
| Approach: | They propose a method to automatically extract keyphrases from research papers using a transformer architecture and graph encoders to incorporate syntactic and semantic dependency graphs. |
| Outcome: | The proposed method achieves state-of-the-art performance on three benchmark datasets. |
Improving Unsupervised Keyphrase Extraction using Background Knowledge (L18-1)
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| Challenge: | Existing methods of keyphrase extraction are supervised and unsupervised . Topical PageRank uses topical information to extract the top topics of a document . |
| Approach: | They propose an unsupervised method for keyphrase extraction based on Wikipedia . they construct a semantic graph and transform the extraction problem into an optimization problem . |
| Outcome: | The proposed method improves over other state-of-the-art models by more than 2% in F1-score. |
SAMRank: Unsupervised Keyphrase Extraction using Self-Attention Map in BERT and GPT-2 (2023.emnlp-main)
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| 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. |
HyperRank: Hyperbolic Ranking Model for Unsupervised Keyphrase Extraction (2023.emnlp-main)
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| Challenge: | Existing unsupervised keyphrase extraction models overlook latent hierarchical structures when extracting keyphrases. |
| Approach: | They propose a new ranking model that models global and local contexts to estimate the importance of each candidate keyphrase within the hyperbolic space. |
| Outcome: | The proposed model outperforms state-of-the-art models in keyphrase extraction tasks. |
On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation (2024.lrec-main)
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| Challenge: | a new study examines the use of encoder-only pre-trained language models in keyphrase generation (KPG) keyphrases are phrases that condense salient information of a document. |
| Approach: | They propose to use encoder-only pre-trained language models in keyphrase generation . they also examine optimal architectural decisions for employing encoder only PLMs in KPG . |
| Outcome: | The proposed model outperforms general-domain seq2seq models in keyphrase generation. |