Abstractive citation text generation is usually framed as an infilling task, where a sequence-to-sequence model is trained to generate a citation given a reference paper and the context window around the target; the generated citation should be a brief discussion of the reference paper as it relates to the citing context. However, examining a recent LED-based citation generation system, we find that many of the generated citations are generic summaries of the reference papers main contribution, ignoring the citation contexts focus on a different topic. To address this problem, we propose a simple modification to the citation text generation task: the generation target is not only the citation itself, but the entire context window, including the target citation. This approach can be easily applied to any abstractive citation generation system, and our experimental results show that training in this way is preferred by human readers and allows the generation model to make use of contextual clues about what topic to discuss and what stance to take.

翻译：摘要：抽象式引用文本生成通常被构建为一种填充任务，即训练序列到序列模型，根据目标论文及其周围的上下文窗口生成引用；生成的引用应是对目标论文的简要讨论，并需与引用上下文相关联。然而，通过分析基于LED的最新引用生成系统，我们发现许多生成的引用仅是对目标论文主要贡献的通用总结，忽视了引用上下文中聚焦的不同主题。为解决这一问题，我们提出对引用文本生成任务进行简单修改：生成目标不仅包括引用本身，而是包含目标引用的整个上下文窗口。该方法可轻松应用于任何抽象式引用生成系统，实验结果表明，这种训练方式更受人类读者青睐，且能使生成模型有效利用上下文线索，明确应讨论的主题以及应采取的立场。