Dynamic topic models (DTMs) analyze text streams to capture the evolution of topics.
Despite their popularity, existing DTMs are either fully supervised, requiring expensive …

Extractive opinion summarization involves automatically producing a summary of text about
an entity (eg, a product's reviews) by extracting representative sentences that capture …

Unsupervised discovery of stories with correlated news articles in real-time helps people
digest massive news streams without expensive human annotations. A common approach of …

Recent pre-trained language models (PLMs) achieve promising results in existing
abstractive summarization datasets. However, existing summarization benchmarks overlap …

Text classification is essential for organizing unstructured text. Traditional methods rely on
human annotations or, more recently, a set of class seed words for supervision, which can …

Continuous learning from an immense volume of data streams becomes exceptionally
critical in the internet era. However, data streams often do not conform to the same …