  Dialogue segmentation is a crucial task for dialogue systems allowing a
better understanding of conversational texts. Despite recent progress in
unsupervised dialogue segmentation methods, their performances are limited by
the lack of explicit supervised signals for training. Furthermore, the precise
definition of segmentation points in conversations still remains as a
challenging problem, increasing the difficulty of collecting manual
annotations. In this paper, we provide a feasible definition of dialogue
segmentation points with the help of document-grounded dialogues and release a
large-scale supervised dataset called SuperDialseg, containing 9,478 dialogues
based on two prevalent document-grounded dialogue corpora, and also inherit
their useful dialogue-related annotations. Moreover, we provide a benchmark
including 18 models across five categories for the dialogue segmentation task
with several proper evaluation metrics. Empirical studies show that supervised
learning is extremely effective in in-domain datasets and models trained on
SuperDialseg can achieve good generalization ability on out-of-domain data.
Additionally, we also conducted human verification on the test set and the
Kappa score confirmed the quality of our automatically constructed dataset. We
believe our work is an important step forward in the field of dialogue
segmentation. Our codes and data can be found from:
https://github.com/Coldog2333/SuperDialseg.
