Abstract

Recent deep learning and sequence-to-sequence learning technology have produced impressive results on automatic summarization. However, the models have limited insights on the underlying language and it remains challenging for system-generated summaries to be truthful to the original input or cover the most important information. This is especially the case for generating abstractive summaries using neural models. My work aims for a flexible and controllable summarization system that can be adapted to cater to different scenarios. It is designed to incorporate linguistic structure information into deep neural networks, have the capability to produce abstracts by re-using a varying amount of source text, and take language characteristics into consideration for summary generation and selection. My dissertation provides a comprehensive overview to the problem of text summarization. I will present a number of approaches to incorporate linguistic structure into state-of-the-art deep neural models to help system summaries remain grammatical and retain the most salient meaning of the source text. I will also describe a summarization approach that is controllable during training and produce diverse summaries during the decoding and re-ranking processes. Finally, I will conclude with a novel approach for selecting optimal summaries from a collection of candidates and discuss the opportunities and challenges in this promising area of research.

Notes

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Graduation Date

2021

Semester

Spring

Advisor

Liu, Fei

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Computer Science

Degree Program

Computer Science

Format

application/pdf

Identifier

CFE0008938; DP0026217

URL

https://purls.library.ucf.edu/go/DP0026217

Language

English

Release Date

11-15-2022

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)

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