Document and discourse segmentation are two fundamental NLP tasks pertaining to
breaking up text into constituents, which are commonly used to help downstream tasks such …

Topic segmentation aims to reveal the latent structure of a document and divide it into
multiple parts. However, current neural solutions are limited in the context modeling of …

Topic segmentation is the process of dividing a text into semantically coherent segments,
and segment labeling involves assigning a topic label to each of these segments. Previous …

Text segmentation (TS) is the process of dividing multi-topic text collections into cohesive
segments using topic boundaries. Similarly, text clustering has been renowned as a major …

Text mining techniques, particularly topic modeling, can be used for the automatic extraction
of information from medical reports. The ability to autonomously analyze texts and identify …

Text segmentation is a fundamental task in natural language processing, where documents
are split into contiguous sections. However, prior research in this area has been constrained …

The Internet, much like our universe, is ever-expanding. Information, in the most varied
formats, is continuously added to the point of information overload. Consequently, the ability …

In the domain of natural language processing and text analysis, the Bidirectional Encoder
Representations from Transformers (BERT) has emerged as a powerful tool for discerning …

Text segmentation is a traditional task in NLP where a document is broken down into
smaller, coherent segments. While several methods and benchmarks exist for well-formed …

Topic segmentation is an important task in the field of natural language processing (NLP),
which finds its importance in applications such as information retrieval, text summarization, e …