In paper, we propose an unsupervised text summarization model which generates a
summary by extracting salient sentences in given document (s). In particular, we model text …

Previous work on automatic summarization does not thoroughly consider coherence while
generating the summary. We introduce a graph-based approach to summarize scientific …

Preprocessing, term selection, term weighting, sentence weighting, and sentence selection
are the main issues in generating extractive summaries of text sentences. Although many …

We present a method which exploits automatically generated scientific discourse
annotations to create a content model for the summarisation of scientific articles. Full papers …

In the last 16 years with the existence of Document Understanding Conference (DUC),
several methods have been developed in Automatic Extractive Text Summarization (AETS) …

Over the last years, several Multi-Document Summarization (MDS) methods have been
presented in Document Understanding Conference (DUC), workshops. Since DUC01 …

Word embedding algorithms have become a common tool in the field of natural language
processing. While some, like Word2Vec, are based on sequential text input, others are …

Automatic text summarization has emerged as a technique for accessing only to useful
information. In order to known the quality of the automatic summaries produced by a system …

Abstract The methods of Automatic Extractive Summarization (AES) uses the features of the
sentences of the original text to extract the most important information that will be considered …

Abstract The Evolutionary Algorithms (EAs) save sufficient data about problem features,
search space, and population information during the runtime. Accordingly, the machine …