# Self-Compositional Data Augmentation for Scientific Keyphrase Generation

arXiv: https://arxiv.org/abs/2411.03039 · HTML: https://mlsanity.com/paper/2411.03039/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 59 references that are in the map.
- First posted on arXiv 5 Nov 2024.

## Builds on (most cited first, 30 of 31)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Good-Enough Compositional Data Augmentation](https://mlsanity.com/paper/1904.09545/) (2019)
- [Deep Keyphrase Generation](https://mlsanity.com/paper/1704.06879/) (2017)
- [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://mlsanity.com/paper/2204.03905/) (2022)
- [SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation](https://mlsanity.com/paper/1808.07512/) (2018)
- [One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases](https://mlsanity.com/paper/1810.05241/) (2018)
- [Unsupervised Keyphrase Extraction with Multipartite Graphs](https://mlsanity.com/paper/1803.08721/) (2018)
- [Keyphrase Generation with Correlation Constraints](https://mlsanity.com/paper/1808.07185/) (2018)
- [KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents](https://mlsanity.com/paper/1911.12559/) (2019)
- [One2Set: Generating Diverse Keyphrases as a Set](https://mlsanity.com/paper/2105.11134/) (2021)
- [Semi-Supervised Learning for Neural Keyphrase Generation](https://mlsanity.com/paper/1808.06773/) (2018)
- [Title-Guided Encoding for Keyphrase Generation](https://mlsanity.com/paper/1808.08575/) (2018)
- [Learning Rich Representation of Keyphrases from Text](https://mlsanity.com/paper/2112.08547/) (2021)
- [An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction](https://mlsanity.com/paper/1904.03454/) (2019)
- [An Empirical Study on Neural Keyphrase Generation](https://mlsanity.com/paper/2009.10229/) (2020)
- [Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention](https://mlsanity.com/paper/2008.01739/) (2020)
- [Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training](https://mlsanity.com/paper/2205.10471/) (2022)
- [Heterogeneous Graph Neural Networks for Keyphrase Generation](https://mlsanity.com/paper/2109.04703/) (2021)
- [Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study](https://mlsanity.com/paper/2212.10233/) (2022)
- [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://mlsanity.com/paper/2103.12440/) (2021)
- [Hierarchical Multi-Label Classification of Scientific Documents](https://mlsanity.com/paper/2211.02810/) (2022)
- [ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization](https://mlsanity.com/paper/2201.05313/) (2022)
- [Data Augmentation for Low-Resource Keyphrase Generation](https://mlsanity.com/paper/2305.17968/) (2023)
- [Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models](https://mlsanity.com/paper/2310.06374/) (2023)
- [Unsupervised Deep Keyphrase Generation](https://mlsanity.com/paper/2104.08729/) (2021)
- [KPDrop: Improving Absent Keyphrase Generation](https://mlsanity.com/paper/2112.01476/) (2021)
- [A Large-Scale Dataset for Biomedical Keyphrase Generation](https://mlsanity.com/paper/2211.12124/) (2022)
- [SenSeNet: Neural Keyphrase Generation with Document Structure](https://mlsanity.com/paper/2012.06754/) (2020)
- [General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation](https://mlsanity.com/paper/2208.09606/) (2022)

## Built on by (most cited first, 1 of 1)

- [Evaluating the Homogeneity of Keyphrase Prediction Models](https://mlsanity.com/paper/2602.12989/) (2026)

## Abstract (arXiv)

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.