Towards Personalized and Human-in-the-Loop Document Summarization

TL;DR

This paper introduces novel personalized and interactive summarization techniques to address information overload, demonstrating improved efficiency over existing models across various domains like network, health, and business data.

Contribution

It presents new methods for feature engineering, flexible summarization, and personalization, advancing the state-of-the-art in document summarization.

Findings

Proposed approaches outperform existing models in efficiency.

Effective summarization across multiple domains.

Enhanced personalization and interactivity in summaries.

Abstract

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.