  An automatic citation generation system aims to concisely and accurately
describe the relationship between two scientific articles. To do so, such a
system must ground its outputs to the content of the cited paper to avoid
non-factual hallucinations. Due to the length of scientific documents, existing
abstractive approaches have conditioned only on cited paper abstracts. We
demonstrate empirically that the abstract is not always the most appropriate
input for citation generation and that models trained in this way learn to
hallucinate. We propose to condition instead on the cited text span (CTS) as an
alternative to the abstract. Because manual CTS annotation is extremely time-
and labor-intensive, we experiment with distant labeling of candidate CTS
sentences, achieving sufficiently strong performance to substitute for
expensive human annotations in model training, and we propose a
human-in-the-loop, keyword-based CTS retrieval approach that makes generating
citation texts grounded in the full text of cited papers both promising and
practical.
