Self-Compositional Data Augmentation for Scientific Keyphrase Generation

• URL: http://arxiv.org/abs/2411.03039v2
• Date: Wed, 06 Nov 2024 09:28:25 GMT
• Title: Self-Compositional Data Augmentation for Scientific Keyphrase Generation
• Authors: Mael Houbre, Florian Boudin, Beatrice Daille, Akiko Aizawa,
• Abstract summary: We present a self-compositional data augmentation method for keyphrase generation. We measure the relatedness of training documents based on their shared keyphrases, and combine similar documents to generate synthetic samples.
• Score: 28.912937922090038
• License:
• Abstract: State-of-the-art models for keyphrase generation require large amounts of training data to achieve good performance. However, obtaining keyphrase-labeled documents can be challenging and costly. To address this issue, we present a self-compositional data augmentation method. More specifically, we measure the relatedness of training documents based on their shared keyphrases, and combine similar documents to generate synthetic samples. The advantage of our method lies in its ability to create additional training samples that keep domain coherence, without relying on external data or resources. Our results on multiple datasets spanning three different domains, demonstrate that our method consistently improves keyphrase generation. A qualitative analysis of the generated keyphrases for the Computer Science domain confirms this improvement towards their representativity property.

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