  Dialogue summarization aims to condense the lengthy dialogue into a concise
summary, and has recently achieved significant progress. However, the result of
existing methods is still far from satisfactory. Previous works indicated that
omission is a major factor in affecting the quality of summarization, but few
of them have further explored the omission problem, such as how omission
affects summarization results and how to detect omission, which is critical for
reducing omission and improving summarization quality. Moreover, analyzing and
detecting omission relies on summarization datasets with omission labels (i.e.,
which dialogue utterances are omitted in the summarization), which are not
available in the current literature. In this paper, we propose the OLDS
dataset, which provides high-quality Omission Labels for Dialogue
Summarization. By analyzing this dataset, we find that a large improvement in
summarization quality can be achieved by providing ground-truth omission labels
for the summarization model to recover omission information, which demonstrates
the importance of omission detection for omission mitigation in dialogue
summarization. Therefore, we formulate an omission detection task and
demonstrate our proposed dataset can support the training and evaluation of
this task well. We also call for research action on omission detection based on
our proposed datasets. Our dataset and codes are publicly available.
