Contextualizing Generated Citation Texts
Biswadip Mandal Xiangci Li Jessica Ouyang
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.