TEXT SUMMARIZATION IN QUANTUM COMPUTING

Abstract

This study explores the classification of paradigms in natural language processing (NLP) tasks, emphasizing the distinction between compositional and distributional approaches. While compositional methods prioritize structural understanding, distributional approaches focus on contextual behaviors. DisCo, a hybrid model integrating both paradigms, shows some hope in overcoming the limitations of traditional compositional and distributional models by incorporating grammatical structures as inputs and leveraging quantum computing principles. This hope is based on the analogy of composing words as entangled states and the meaning extraction as information flow between entangled states. In this dissertation, we show that this hope might not be realized on the current and near future quantum hardware. Our practical implementation reveals challenges from the model in handling longer texts and issues with handling semantic composition, especially in distinguishing whether a simple sentence is a summary of a simple paragraph. We offer insights on why such task is hard for near future quantum hardware in general, and if some solutions are realized they may not overcome classical NLP in terms of computational resources.