Macquarie University
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Towards personalised and human-in-the-loop document summarisation

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posted on 2022-08-18, 04:02 authored by Samira GhodratnamaSamira Ghodratnama

The ubiquitous availability of computing devices and the widespread use of the internet have generated a large amount of data continuously. Therefore, the amount of available information on any given topic is far beyond humans’ processing capacity to properly process, causing what is known as ‘information overload’. To efficiently cope with large amounts of information and generate content with significant value to users, we require identifying, merging and summarising information. Data summaries can help gather related information and collect it into a shorter format that enables answering complicated questions, gaining new insight and discovering conceptual boundaries. This thesis focuses on three main challenges to alleviate information overload using novel summarisation techniques. It further intends to facilitate the analysis of documents to support personalised information extraction. This thesis separates the research issues into four areas, covering (i) feature engineering in document summarisation, (ii) traditional static and inflexible summaries, (iii) traditional generic summarisation approaches, and (iv) the need for reference summaries. We propose novel approaches to tackle these challenges, by: • enabling automatic intelligent feature engineering • enabling flexible and interactive summarisation • utilising intelligent and personalised summarisation approaches. The experimental results prove the efficiency of the proposed approaches compared to other state-of-the-art models. We further propose solutions to the information overload problem in different domains through summarisation, covering network traffic data, health data and business process data.


Table of Contents

1 Introduction – 2 Background and related work – 3 Experimental setup – 4 Towards intelligent feature engineering – 5 Towards interactive document summarisation – 6 Towards personalized document summarisation – 7 Summarisation applications – 8 Conclusion and feature work -- References


A thesis submitted to Macquarie University for the degree of Doctor of Philosophy

Awarding Institution

Macquarie University

Degree Type

Thesis PhD


Thesis (PhD), Department of Computing, Faculty of Science and Engineering, Macquarie University

Department, Centre or School

Department of Computing

Year of Award


Principal Supervisor

Amin Beheshti

Additional Supervisor 1

Jia Wu


Copyright: The Author Copyright disclaimer:




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