Knowledge representation learning (KRL) aims to represent entities and relations in
knowledge graph in low-dimensional semantic space, which have been widely used in …

With the evolution of the Internet and multimedia technology, the amount of text data has
increased exponentially. This text volume is a precious source of information and knowledge …

Extractive text summarization aims to create a condensed version of one or more source
documents by selecting the most informative sentences. Research in text summarization has …

Due to its promise to alleviate information overload, text summarization has attracted the
attention of many researchers. However, it has remained a serious challenge. Here, we first …

With the problem of increased web resources and the huge amount of information available,
the necessity of having automatic summarization systems appeared. Since summarization is …

Abstract Knowledge graphs (KGs) are intrinsically incomplete. Knowledge representation
learning methods deal with embedding components of a knowledge graph (ie, entities and …

The increasing amount of biomedical information that is available for researchers and
clinicians makes it harder to quickly find the right information. Automatic summarization of …

Research about text summarization has been quite an interesting topic over the years,
proven by numerous number of papers related with discussion of their studies such as …

Patterns are used as a fundamental means to analyse data in many text mining applications.
Many efficient techniques have been developed to discover patterns. However, the …

We show that surface-level markers of pragmatic intent can be used to recognize the
important sentences in text and can thereby improve the performance of text summarization …