| Challenge: | Existing approaches for keyphrase generation generate uncontrollable and inaccurate absent keyphrases. |
| Approach: | They propose a graph-based method that captures explicit knowledge from related references. |
| Outcome: | The proposed model improves on baseline keyphrase generation models on multiple benchmarks. |
Similar Papers
Mitigating Over-Generation for Unsupervised Keyphrase Extraction with Heterogeneous Centrality Detection (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing keyphrase extraction models incorrectly determine a keyphrase as a phrase but output other candidates as keyphrases because they contain the same word. |
| Approach: | They propose a new approach that detects both implicit and explicit centrality within a heterogeneous graph as the importance score of each candidate keyphrase. |
| Outcome: | The proposed approach outperforms state-of-the-art keyphrase extraction models on three benchmark datasets. |
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction (N19-1)
Copied to clipboard
| Challenge: | Existing methods on keyphrase generation are purely extractive or generative . however, extractive methods cannot predict absent keyphrases which are not in the document. |
| Approach: | They propose a multi-task learning framework that jointly learns an extractive model and a generative model. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on five keyphrase generation tasks. |
Exclusive Hierarchical Decoding for Deep Keyphrase Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to generate keyphrases ignore hierarchical compositionality of keyphrase set and generate duplicated keyphrase sets. |
| Approach: | They propose a hierarchical decoding framework that explicitly models hierarchic compositionality of a keyphrase set and either a soft or a hard exclusion mechanism to enhance the diversity of the generated keyphrases. |
| Outcome: | The proposed framework generates less duplicated and more accurate keyphrases on a set of keyphrase sets. |
SGG: Learning to Select, Guide, and Generate for Keyphrase Generation (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories. |
| Approach: | They propose to deal with present and absent keyphrases separately with different mechanisms by using a hierarchical neural network with a pointing-based selector and a selection-guided generator. |
| Outcome: | The proposed model outperforms baselines on four keyphrase generation tasks and shows extensibility in natural language generation tasks. |
Semi-Supervised Learning for Neural Keyphrase Generation (D18-1)
Copied to clipboard
| Challenge: | Existing models for keyphrase generation only use labeled data, which is limited to resource-rich domains. |
| Approach: | They propose semi-supervised keyphrase generation methods by leveraging labeled data and large-scale unlabeled samples for learning. |
| Outcome: | The proposed methods outperform state-of-the-art models trained with labeled data and large-scale unlabeled samples for learning. |
Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention (2021.acl-long)
Copied to clipboard
| Challenge: | Generally, documents are truncated before being inputs to deep neural networks, resulting in missing keyphrases . evaluators use layer-wise coverage attention to cover all the critical points in a document . |
| Approach: | They propose a neural keyphrase generation model that identifies the salient sentences in a document and an extractor-generator that jointly extracts and generates keyphrases from the selected sentences. |
| Outcome: | The proposed model outperforms the state-of-the-art keyphrase generation methods on keyphrases generated from scientific and web documents. |
Keyphrase Generation Beyond the Boundaries of Title and Abstract (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Current approaches to keyphrase generation use only the title and abstract of the articles. |
| Approach: | They propose to integrate full text and semantically similar articles to generate keyphrases from a dataset that includes the full text of the articles along with the title and abstract. |
| Outcome: | The proposed model can generate keyphrases that are present or absent from the text. |
Heterogeneous Graph Transformer for Graph-to-Sequence Learning (2020.acl-main)
Copied to clipboard
| Challenge: | Recent studies ignore the indirect relations between distance nodes, or treat indirect relations and direct relations in the same way. |
| Approach: | They propose a graph-to-sequence (Graph2Seq) encoder which models graph structure to model different relations in individual subgraphs of the original graph. |
| Outcome: | The proposed model outperforms the state-of-the-art on all four benchmarks of AMR-to-text generation and syntax-based neural machine translation. |
On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation (2024.lrec-main)
Copied to clipboard
| 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. |
ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for keyphrase prediction rely on heuristicically defined importance scores . existing methods lack consideration for time efficiency . |
| Approach: | They propose an unsupervised keyphrase generation model that combines informativeness and phraseness modules. |
| Outcome: | The proposed model outperforms baseline models and achieves 89% of the performance of a supervised model for top 10 predictions. |