  Topic segmentation and outline generation strive to divide a document into
coherent topic sections and generate corresponding subheadings, unveiling the
discourse topic structure of a document. Compared with sentence-level topic
structure, the paragraph-level topic structure can quickly grasp and understand
the overall context of the document from a higher level, benefitting many
downstream tasks such as summarization, discourse parsing, and information
retrieval. However, the lack of large-scale, high-quality Chinese
paragraph-level topic structure corpora restrained relative research and
applications. To fill this gap, we build the Chinese paragraph-level topic
representation, corpus, and benchmark in this paper. Firstly, we propose a
hierarchical paragraph-level topic structure representation with three layers
to guide the corpus construction. Then, we employ a two-stage man-machine
collaborative annotation method to construct the largest Chinese
Paragraph-level Topic Structure corpus (CPTS), achieving high quality. We also
build several strong baselines, including ChatGPT, to validate the
computability of CPTS on two fundamental tasks (topic segmentation and outline
generation) and preliminarily verified its usefulness for the downstream task
(discourse parsing).
