Towards Personalized and Human-in-the-Loop Document Summarization
  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: i)enabling automatic intelligent feature engineering, ii)
enabling flexible and interactive summarisation, iii) 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.
