| Challenge: | Existing keyphrase generation methods generate overlapping phrases (including sub-phrases or super-phrase) Existing methods are far from satisfactory for a wide range of natural language processing tasks. |
| Approach: | They propose a parallel Seq2Seq network with coverage attention to alleviate the overlapping phrase problem by integrating linguistic constraints of keyphrase into the basic Seq2-Sequeq network on the source side and employ the multi-task learning framework on the target side. |
| Outcome: | The proposed method outperforms the state-of-the-art CopyRNN on scientific datasets and is also more effective in news domain. |
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| Challenge: | Existing methods for keyphrase generation ignore correlation among keyphrases, resulting in duplication and coverage issues. |
| Approach: | They propose a new sequence-to-sequence architecture for keyphrase generation that captures correlation among keyphrases by preceding phrases to eliminate duplicate phrases and improve result coherence. |
| Outcome: | The proposed model outperforms the state-of-the-art method on benchmark datasets in terms of accuracy and diversity. |
Topic-Aware Neural Keyphrase Generation for Social Media Language (P19-1)
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| 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 . |
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Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention (2021.acl-long)
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| 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. |
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Automatic Keyphrase Generation by Incorporating Dual Copy Mechanisms in Sequence-to-Sequence Learning (2022.coling-1)
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| Challenge: | Existing models for keyphrase generation use a copy mechanism to generate keyphrases, but they do not identify key words in the source text and copy them to create more keyphrase. |
| Approach: | They propose a dual-copier keyphrase generation model that uses a sequence-to-sequence model to generate keyphrases for a piece of text. |
| Outcome: | The proposed model outperforms baseline models and achieves an obvious performance improvement. |
SGG: Learning to Select, Guide, and Generate for Keyphrase Generation (2021.naacl-main)
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| 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. |
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Semi-Supervised Learning for Neural Keyphrase Generation (D18-1)
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| 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. |
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction (N19-1)
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| 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. |
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Heterogeneous Graph Neural Networks for Keyphrase Generation (2021.emnlp-main)
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| 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. |
Keyphrase Generation Beyond the Boundaries of Title and Abstract (2022.findings-emnlp)
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| 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. |
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Multi-task Learning for Paraphrase Generation With Keyword and Part-of-Speech Reconstruction (2022.findings-acl)
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| Challenge: | PGKPR is a deep learning approach to generate paraphrases using key semantics of the source sentence. |
| Approach: | They propose a model with keyword and part-of-speech reconstruction for paraphrase generation using deep learning. |
| Outcome: | The proposed model outperforms comparative models on two commonly-used datasets. |