Abstractive multi-document summarization - paraphrasing and compressing with neural networks

This thesis presents studies in neural text summarization for single and multiple documents.The focus is on using sentence paraphrasing and compression for generating fluent summaries, especially in multi-document summarization where there is data paucity. A novel solution is to use transfer-learning from downstream tasks with an abundance of data. For this purpose, we pre-train three models for each of extractive summarization, paraphrase generation and sentence compression. We find that summarization datasets – CNN/DM and NEWSROOM – contain a number of noisy samples. Hence, we present a method for automatically filtering out this noise. We combine the representational power of the GRU-RNN and TRANSFORMER encoders in our paraphrase generation model. In training our sentence compression model, we investigate the impact of using different early-stopping criteria, such as embedding-based cosine similarity and F1. We utilize the pre-trained models (ours, GPT2 and T5) in different settings for single and multi-document summarization.

Authors

• Egonmwan, Elozino Ofualagba
• University of Lethbridge. Faculty of Arts and Science

• Artificial intelligence
• Automatic programming (Computer science)
• Computer programming
• Dissertations, Academic
• Machine learning
• Natural language processing (Computer science)
• Neural networks (Computer science)

Identifier

hdl:10133/5827

OAI identifier oai:identifier

oai:opus.uleth.ca:10133/5827