| 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. |
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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. |
| Outcome: | The proposed model outperforms the state-of-the-art keyphrase generation methods on keyphrases generated from scientific and web documents. |
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. |
Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts (N18-1)
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| 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. |
Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards (P19-1)
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| Challenge: | Existing generative models generate too few keyphrases, but they often generate too many . et al. (2017) propose a reinforcement learning approach for keyphrase generation . |
| Approach: | They propose a reinforcement learning approach that encourages a model to generate sufficient keyphrases with an adaptive reward function. |
| Outcome: | The proposed method improves state-of-the-art generative models with conventional and new evaluation methods on real-world datasets. |
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. |
| Outcome: | The proposed model outperforms baselines on four keyphrase generation tasks and shows extensibility in natural language generation tasks. |
Incorporating Linguistic Constraints into Keyphrase Generation (P19-1)
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| 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. |
Keyphrase Generation: A Text Summarization Struggle (N19-1)
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| Challenge: | Existing methods for keyphrase generation are unable to produce valuable terms that do not appear in the text. |
| Approach: | They propose to consider the keyphrase string as an abstractive summary of the title and the abstract. |
| Outcome: | The proposed method can generate better keyphrases than the existing methods or the unsupervised ones. |
Paraphrase Generation: A Survey of the State of the Art (2021.emnlp-main)
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| Challenge: | Using neural models, paraphrase generation research has shifted to neural methods . a recent study focused on paraphrases, which are used in language understanding tasks . |
| Approach: | They propose to use neural methods to generate fluent, diverse paraphrases from a sentence . they propose to combine large pretrained language models with other mechanisms to generate more advanced paraphrase generation models. |
| Outcome: | This paper examines various approaches to paraphrase generation with a main focus on neural methods. |
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. |
An Empirical Study on Neural Keyphrase Generation (2021.naacl-main)
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| Challenge: | Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. |
| Approach: | They propose to compare the generalizability of KPG models with other models by analyzing the most crucial factors that may affect their generalizarability. |
| Outcome: | The proposed model can be used to predict keyphrases from a set of input sequences, and it can be compared with existing models. |