CiteBART: Learning to Generate Citations for Local Citation Recommendation

• URL: http://arxiv.org/abs/2412.17534v1
• Date: Mon, 23 Dec 2024 12:58:30 GMT
• Title: CiteBART: Learning to Generate Citations for Local Citation Recommendation
• Authors: Ege Yiğit Çelik, Selma Tekir,
• Abstract summary: This paper proposes CiteBART, a custom BART pre-training based on citation token masking to generate citations to achieve local citation recommendation (LCR) In the base scheme, we mask the citation token in the local citation context to make the citation prediction. In the global one, we reconstruct the paper's title and abstract to the local citation context to learn to the citation token. The effect is significant in the larger benchmarks, e.g., Refseer and ArXiv.
• Score: 0.138120109831448
• License:
• Abstract: Citations are essential building blocks in scientific writing. The scientific community is longing for support in their generation. Citation generation involves two complementary subtasks: Determining the citation worthiness of a context and, if it's worth it, proposing the best candidate papers for the citation placeholder. The latter subtask is called local citation recommendation (LCR). This paper proposes CiteBART, a custom BART pre-training based on citation token masking to generate citations to achieve LCR. In the base scheme, we mask the citation token in the local citation context to make the citation prediction. In the global one, we concatenate the citing paper's title and abstract to the local citation context to learn to reconstruct the citation token. CiteBART outperforms state-of-the-art approaches on the citation recommendation benchmarks except for the smallest FullTextPeerRead dataset. The effect is significant in the larger benchmarks, e.g., Refseer and ArXiv. We present a qualitative analysis and an ablation study to provide insights into the workings of CiteBART. Our analyses confirm that its generative nature brings about a zero-shot capability.

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