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Xing, Linzi

University of British Columbia

Topic segmentation, as a fundamental NLP task, has been proposed and systematically studied since the 1980s and received increased attention in recent years due to the surge in big data. It aims to unveil the coarse-grained semantic structure of long unstructured documents by automatically dividing them into shorter, topically coherent segments.The coarse-grained structure provided by topic segmentation has been proven to not only enhance human reading efficiency but also play a vital role in other natural language understanding tasks, such as text summarization, question answering, and dialogue modeling. Before the neural era, early computational models for topic segmentation typically adhered to unsupervised paradigms with lexical cohesion directly derived from the input, yet their performance was notably limited. With the evolution of deep learning and enhanced computational capabilities, neural models have delivered significant progress in performance. Nevertheless, inadequate coherence modeling, in terms of both explicitness and reliability in these neural approaches, prevents them from emerging as more accurate and robust solutions for topic segmentation. Additionally, the growing prevalence of multi-modal data content across social media platforms has heightened the need for topic segmentation to traverse beyond mere text, extending into videos. Motivated by the challenges and needs mentioned above, in this thesis, we direct our efforts towards enhancing neural topic segmentation for two types of documents: text and video. To overcome the inadequate coherence modeling (explicitness and reliability) in neural topic segmenters for text, we propose a series of methods that either more explicitly model coherence patterns or leverage coherence signals encoded in related auxiliary tasks, notably discourse parsing and language modeling. For video content, we explore to extend neural topic segmenters, originally designed for text, into a multi-modal setting which is also robust to the often-encountered drastic variance in video length. A comprehensive set of experimental results indicates that our methods not only effectively enhance the overall performance of neural segmenters for text and video in intra-domain scenarios, but also broaden their applicability to data in other domains.

Text

2024-02-23

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/87475

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/