Extractive summaries are usually presented as lists of sentences with no expected cohesion
between them and with plenty of redundant information if not accounted for. In this paper, we …

With the evolving and interdisciplinary nature of research, there is a need to facilitate and
assist researchers in the manual process of building a literature review. This paper proposes …

Document summarization involves identifying the salient text in a document and creating a
representative summary. The event-index cognitive model describes the human cognitive …

Semantic role labeling (SRL), which extracts shallow semantic relation representation from
different surface textual forms of free text sentences, is important for understanding clinical …

Extractive summaries are usually presented as lists of sentences with no expected cohesion
between them and with plenty of redundant information if not accounted for. In this paper, we …

Purpose: The naive bayes algorithm is one of the most popular machine learning algorithms,
because it is simple, has high computational efficiency and has good accuracy. The naive …

We present improvements to our incremental proposition-based summariser, which is
inspired by Kintsch and van Dijk's (1978) text comprehension model. Argument overlap is a …

Summarization has remained as an old idea of representing and expressing important facts
and ideas in a shorter format without distorting the main context of the content at which they …

Summarization systems face the core challenge of identifying and selecting important
information. In this paper, we tackle the problem of content selection in unsupervised …

While great strides have been made with natural language processing (NLP) techniques in
the last few decades, there has been a notable lack of research into utilizing NLP for the …