Towards a Unified Framework for Aspect-based Multi-document Text Summarization

Confronted with expansive bodies of text, the fastest way to glean central pieces of information is usually a summary, condensing the most relevant points into a shorter piece of text. However, the manual curation of high-quality text summaries is a laborious and time-intensive task, requiring intense focus and attention. This motivates the central topic of this thesis: the automatic generation of textual summaries. Instead of relying on humans, we intend to summarize texts with the help of algorithms, designed to capture the central importance. Yet, despite decades of research into automatic text summarization systems, we are still not at a point where the resulting algorithms could provide the basis for a product that sees large-scale adoption by the general public.

This thesis focuses on this obvious gap and provides a fundamental framework to address some of the remaining shortcomings in automatic text summarization systems, which we divide into three central problems.

1. High-quality, task-specific data remains a scarce resource, particularly for languages besides English.
2. Existing works over-index on narrow domains, such as news summarization, lacking inclusion of user-centric perspectives for summary generation.
3. A lack of comprehensive and meaningful evaluations of text summarization systems.