Show simple item record

dc.contributor.advisorLi, Weihua
dc.contributor.advisorYongchareon, Sira
dc.contributor.authorHellesoe, Lui Joseph
dc.date.accessioned2022-07-04T01:00:43Z
dc.date.available2022-07-04T01:00:43Z
dc.date.copyright2022
dc.identifier.urihttp://hdl.handle.net/10292/15272
dc.description.abstractText summarisation has been recognised as a critical Natural Language Processing task, attracting significant attention from researchers and practitioners. Various domains have widely adopted it. For example, text summarisation of news, articles and book chapters can produce a short text, assisting the readers with grasping the main idea rapidly. In the medical domain, practitioners apply it to summarise their questions, and in the legal domain, practitioners also use it for summarising Judges decisions. However, it is challenging to control the summarised output by producing domainspecific summaries since the focus of domain-specific information may be ignored. In recent years, automatic text summarisation has become a hot research topic. This thesis proposes a novel approach for domain-specific document automatic text summarisation. With this, users can realise more efficient reading and understanding of the main contents of a document after the summarisation. In order to solve the problem of domain-specific summarisation, we propose a hybrid model that combines three kinds of embedding approaches: domain, focus and context embeddings. We apply the proposed approach to the MeQSum and LegalCosts datasets for evaluating the performance and effectiveness of using hybrid embeddings for specialised document summarisation. The experimental results demonstrate that our model outperforms state-of-the-art algorithms in automation and summary quality.en_NZ
dc.language.isoenen_NZ
dc.publisherAuckland University of Technology
dc.titleAutomatic Domain-Specific Text Summarisation With Deep Learning Approachesen_NZ
dc.typeThesisen_NZ
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelMasters Theses
thesis.degree.nameMaster of Computer and Information Sciencesen_NZ
dc.rights.accessrightsOpenAccess
dc.date.updated2022-07-03T19:25:35Z


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record