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        <title>Representation Learning for Resource-Constrained Keyphrase Generation</title>
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    <abstract>State-of-the-art keyphrase generation methods generally depend on large annotated datasets, limiting their performance in domains with limited annotated data. To overcome this challenge, we design a data-oriented approach that first identifies salient information using retrieval-based corpus-level statistics, and then learns a task-specific intermediate representation based on a pre-trained language model using large-scale unlabeled documents. We introduce salient span recovery and salient span prediction as denoising training objectives that condense the intra-article and inter-article knowledge essential for keyphrase generation. Through experiments on multiple keyphrase generation benchmarks, we show the effectiveness of the proposed approach for facilitating low-resource keyphrase generation and zero-shot domain adaptation. Our method especially benefits the generation of absent keyphrases, approaching the performance of models trained with large training sets.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.49</identifier>
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%0 Conference Proceedings
%T Representation Learning for Resource-Constrained Keyphrase Generation
%A Wu, Di
%A Ahmad, Wasi
%A Dev, Sunipa
%A Chang, Kai-Wei
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F wu-etal-2022-representation
%X State-of-the-art keyphrase generation methods generally depend on large annotated datasets, limiting their performance in domains with limited annotated data. To overcome this challenge, we design a data-oriented approach that first identifies salient information using retrieval-based corpus-level statistics, and then learns a task-specific intermediate representation based on a pre-trained language model using large-scale unlabeled documents. We introduce salient span recovery and salient span prediction as denoising training objectives that condense the intra-article and inter-article knowledge essential for keyphrase generation. Through experiments on multiple keyphrase generation benchmarks, we show the effectiveness of the proposed approach for facilitating low-resource keyphrase generation and zero-shot domain adaptation. Our method especially benefits the generation of absent keyphrases, approaching the performance of models trained with large training sets.
%R 10.18653/v1/2022.findings-emnlp.49
%U https://aclanthology.org/2022.findings-emnlp.49
%U https://doi.org/10.18653/v1/2022.findings-emnlp.49
%P 700-716

Markdown (Informal)

[Representation Learning for Resource-Constrained Keyphrase Generation](https://aclanthology.org/2022.findings-emnlp.49) (Wu et al., Findings 2022)

• Representation Learning for Resource-Constrained Keyphrase Generation (Wu et al., Findings 2022)

ACL

• Di Wu, Wasi Ahmad, Sunipa Dev, and Kai-Wei Chang. 2022. Representation Learning for Resource-Constrained Keyphrase Generation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 700–716, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.