Improving Abstractive Summarization and Information Consistency Assessment
type: CreativeWork
description: Summarization is the process of compressing a document into a shorter form that contains all the relevant information. It is useful in many applications, for example, a brief summary of the content of an article or a podcast can help decide whether it should be read or listened to. This summarization process is often split into two distinct research areas: extractive summarization, which pulls out key phrases directly from the text; and abstractive summarization, which rephrases and condenses ideas from the text. This thesis examines abstractive summarization as it can provide a more effective and compact summary of the information than extractive summarization. In common with other research areas in natural language processing, deep learning has become the dominant technology in abstractive summarization demonstrating significant performance gains over more traditional approaches. Initially, these neural-based models were trained from scratch, with randomly initialized parameters, relying solely on supervised training. As this meant there was usually limited training data, the generated summaries often had limited diversity and fluency. The recent shift to using foundation models, trained on vast quantities of text exploiting self-supervised training schemes, addressed some of these issues such as fluency. However, there still remain issues such as how to handle long documents. A key challenge to developing these abstractive summarization systems is assessment. In common with many natural language generation tasks, it is not possible to generate an exhaustive set of reference summarises, so simple lexicon-matching approaches have limited accuracy. This thesis investigates both the generation and assessment of abstractive summaries. In addition, approaches motivated by summary assessment are applied to hallucination detection in large language models. The first area examined in this thesis includes two aspects of summary generation. The first aspect is associated wi…
abstract: Summarization is the process of compressing a document into a shorter form that contains all the relevant information. It is useful in many applications, for example, a brief summary of the content of an article or a podcast can help decide whether it should be read or listened to. This summarization process is often split into two distinct research areas: extractive summarization, which pulls out key phrases directly from the text; and abstractive summarization, which rephrases and condenses ideas from the text. This thesis examines abstractive summarization as it can provide a more effective and compact summary of the information than extractive summarization. In common with other research areas in natural language processing, deep learning has become the dominant technology in abstractive summarization demonstrating significant performance gains over more traditional approaches. Initially, these neural-based models were trained from scratch, with randomly initialized parameters, relying solely on supervised training. As this meant there was usually limited training data, the generated summaries often had limited diversity and fluency. The recent shift to using foundation models, trained on vast quantities of text exploiting self-supervised training schemes, addressed some of these issues such as fluency. However, there still remain issues such as how to handle long documents. A key challenge to developing these abstractive summarization systems is assessment. In common with many natural language generation tasks, it is not possible to generate an exhaustive set of reference summarises, so simple lexicon-matching approaches have limited accuracy. This thesis investigates both the generation and assessment of abstractive summaries. In addition, approaches motivated by summary assessment are applied to hallucination detection in large language models. The first area examined in this thesis includes two aspects of summary generation. The first aspect is associated wi…
author: Manakul, Potsawee
language: eng
keywords: automatic evaluation, information consistency, natural language processing, summarization
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/86d0fa8e-d678-4826-9037-10f1327c34ac/download
license: https://creativecommons.org/licenses/by/4.0/
date published: 2024-02-29
date modified: 2026-07-22T22:24:17Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.110012