Topic segmentation is the process of dividing a text into semantically coherent segments, and segment labeling involves assigning a topic label to each of these segments. Previous work on this task has included the use of sequence labeling, segment-extraction, and generative models. While these methods have yielded impressive results, existing generative models have struggled to accurately generate strings of segment boundaries, limiting their competitiveness in this area. In this paper, we present a novel Sequence-to-Sequence approach with Mixed Pointers (Seq2Seq-MP). Seq2Seq-MP employs an encoder-decoder architecture with the pointer mechanism to generate both segment boundaries and topics, which allows for a more robust performance than string-generation models and can handle long-range dependencies better than sequence labeling and segment-extraction models. Additionally, we introduce the pairwise type encoding and type-aware relative position encoding to improve the fusion of type and position information, enhancing the interactions between sentences and topics in the encoder and decoder. Our experiments on public datasets show that Seq2Seq-MP outperforms the current state-of-the-art, with up to 2.9% and 4.0% improvements in Pk and F1, respectively.

Dialogue topic segmentation is a challenging task in which dialogues are split into segments with pre-defined topics. Existing works on topic segmentation adopt a two-stage paradigm, including text segmentation and segment labeling. However, such methods tend to focus on the local context in segmentation, and the inter-segment dependency is not well captured. Besides, the ambiguity and labeling noise in dialogue segment bounds bring further challenges to existing models. In this work, we propose the Parallel Extraction Network with Neighbor Smoothing (PEN-NS) to address the above issues. Specifically, we propose the parallel extraction network to perform segment extractions, optimizing the bipartite matching cost of segments to capture inter-segment dependency. Furthermore, we propose neighbor smoothing to handle the segment-bound noise and ambiguity. Experiments on a dialogue-based and a document-based topic segmentation dataset show that PEN-NS outperforms state-the-of-art models significantly.