Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation
This work addresses the problem of breaking down long texts into coherent segments for improved readability and downstream applications like summarization, representing an incremental advance with novel method integration.
The paper tackles text segmentation by introducing a supervised model that integrates explicit coherence modeling with a two-level Transformer architecture, achieving state-of-the-art performance on benchmark datasets and demonstrating zero-shot language transfer capabilities.
Breaking down the structure of long texts into semantically coherent segments makes the texts more readable and supports downstream applications like summarization and retrieval. Starting from an apparent link between text coherence and segmentation, we introduce a novel supervised model for text segmentation with simple but explicit coherence modeling. Our model -- a neural architecture consisting of two hierarchically connected Transformer networks -- is a multi-task learning model that couples the sentence-level segmentation objective with the coherence objective that differentiates correct sequences of sentences from corrupt ones. The proposed model, dubbed Coherence-Aware Text Segmentation (CATS), yields state-of-the-art segmentation performance on a collection of benchmark datasets. Furthermore, by coupling CATS with cross-lingual word embeddings, we demonstrate its effectiveness in zero-shot language transfer: it can successfully segment texts in languages unseen in training.