# Unsupervised Domain Adaptation for Keyphrase Generation using Citation Contexts

arXiv: https://arxiv.org/abs/2409.13266 · HTML: https://mlsanity.com/paper/2409.13266/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 61 references that are in the map.
- First posted on arXiv 20 Sep 2024.

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

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [Deep Keyphrase Generation](https://mlsanity.com/paper/1704.06879/) (2017)
- [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 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)
- [unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network](https://mlsanity.com/paper/2303.14957/) (2023)
- [Keyphrase Generation for Scientific Document Retrieval](https://mlsanity.com/paper/2106.14726/) (2021)
- [CitationIE: Leveraging the Citation Graph for Scientific Information Extraction](https://mlsanity.com/paper/2106.01560/) (2021)
- [The ACL OCL Corpus: Advancing Open Science in Computational Linguistics](https://mlsanity.com/paper/2305.14996/) (2023)
- [CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision](https://mlsanity.com/paper/2205.06207/) (2022)
- [Representation Learning for Resource-Constrained Keyphrase Generation](https://mlsanity.com/paper/2203.08118/) (2022)
- [LDKP: A Dataset for Identifying Keyphrases from Long Scientific Documents](https://mlsanity.com/paper/2203.15349/) (2022)
- [Keyphrase Generation Beyond the Boundaries of Title and Abstract](https://mlsanity.com/paper/2112.06776/) (2021)
- [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://mlsanity.com/paper/2103.12440/) (2021)
- [A Benchmark of PDF Information Extraction Tools using a Multi-Task and Multi-Domain Evaluation Framework for Academic Documents](https://mlsanity.com/paper/2303.09957/) (2023)
- [Large-Scale Evaluation of Keyphrase Extraction Models](https://mlsanity.com/paper/2003.04628/) (2020)
- [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)
- [Diverse Keyphrase Generation with Neural Unlikelihood Training](https://mlsanity.com/paper/2010.07665/) (2020)
- [A Large-Scale Dataset for Biomedical Keyphrase Generation](https://mlsanity.com/paper/2211.12124/) (2022)
- [General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation](https://mlsanity.com/paper/2208.09606/) (2022)

## Built on by (most cited first, 4 of 4)

- [Self-Compositional Data Augmentation for Scientific Keyphrase Generation](https://mlsanity.com/paper/2411.03039/) (2024)
- [ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation](https://mlsanity.com/paper/2505.24219/) (2025)
- [An Analysis of Datasets, Metrics and Models in Keyphrase Generation](https://mlsanity.com/paper/2506.10346/) (2025)
- [Evaluating the Homogeneity of Keyphrase Prediction Models](https://mlsanity.com/paper/2602.12989/) (2026)

## Abstract (arXiv)

Adapting keyphrase generation models to new domains typically involves few-shot fine-tuning with in-domain labeled data. However, annotating documents with keyphrases is often prohibitively expensive and impractical, requiring expert annotators. This paper presents silk, an unsupervised method designed to address this issue by extracting silver-standard keyphrases from citation contexts to create synthetic labeled data for domain adaptation. Extensive experiments across three distinct domains demonstrate that our method yields high-quality synthetic samples, resulting in significant and consistent improvements in in-domain performance over strong baselines.