Narratives are fundamental to our understanding of the world, providing us with a natural
structure for knowledge representation over time. Computational narrative extraction is a …

Narrative understanding involves capturing the author's cognitive processes, providing
insights into their knowledge, intentions, beliefs, and desires. Although large language …

Timeline Summarization identifies major events from a news collection and describes them
following temporal order, with key dates tagged. Previous methods generally generate …

Narratives are fundamental to our perception of the world and are pervasive in all activities
that involve the representation of events in time. Yet, modern online information systems do …

The development of deep neural networks and the emergence of pre-trained language
models such as BERT allow to increase performance on many NLP tasks. However, these …

Narrative sensemaking is an essential part of understanding sequential data. Narrative
maps are a visual representation model that can assist analysts to understand narratives. In …

Summarizing text-rich documents has been long studied in the literature, but most of the
existing efforts have been made to summarize a static and predefined multi-document set …

The rise of social media and the explosion of digital news in the web sphere have created
new challenges to extract knowledge and make sense of published information. Automated …

Despite significant advances in web archive infrastructures, the problem of exploring the
historical heritage preserved by web archives is yet to be solved. Timeline generation …

Online social media platforms, such as Twitter, provide valuable information during disaster
events. Existing extractive tweet disaster summarization approaches provide a summary of …