自动推荐论文引用并生成符合审稿人偏好的引用句
SCIRGC: Multi-Granularity Citation Recommendation and Citation Sentence Preference Alignment
- 结合引文网络与作者意图，精准推荐相关论文
- 利用原文摘要和上下文生成高质量引用句
- 新评估指标确保引用句贴合学术表达习惯
引文在科研论文中至关重要，用于连接当前研究与已有成果，但人工添加耗时。本文提出SciRGC框架，实现论文引用的自动化推荐与引用句生成。针对两大挑战：一是准确识别作者引文意图并检索相关文献，二是生成符合人类偏好高质量引用句。通过引入引文网络与情感意图提升推荐准确率；在生成模块中，以原文摘要、局部上下文、引文意图及推荐文献为输入，生成基于推理的引用句。同时提出新评估指标，公平衡量生成句质量。与基线模型对比及消融实验表明，该框架显著提升引文推荐的准确性与相关性，并确保生成句在语境中的恰当性，为跨学科研究者提供有力工具。
原文摘要 · Abstract (English)
Citations are crucial in scientific research articles as they highlight the connection between the current study and prior work. However, this process is often time-consuming for researchers. In this study, we propose the SciRGC framework, which aims to automatically recommend citation articles and generate citation sentences for citation locations within articles. The framework addresses two key challenges in academic citation generation: 1) how to accurately identify the author's citation intent and find relevant citation papers, and 2) how to generate high-quality citation sentences that align with human preferences. We enhance citation recommendation accuracy in the citation article recommendation module by incorporating citation networks and sentiment intent, and generate reasoning-based citation sentences in the citation sentence generation module by using the original article abstract, local context, citation intent, and recommended articles as inputs. Additionally, we propose a new evaluation metric to fairly assess the quality of generated citation sentences. Through comparisons with baseline models and ablation experiments, the SciRGC framework not only improves the accuracy and relevance of citation recommendations but also ensures the appropriateness of the generated citation sentences in context, providing a valuable tool for interdisciplinary researchers.
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