Graph-based summarization entails extracting a worthwhile subset of sentences from a
collection of textual documents by using a graph-based model to represent the correlations …

The explosive growth of textual data on the web and the problem of obtaining desired
information through this enormous volume of data has led to a dramatic increase in demand …

Huge volumes of textual information has been produced every single day. In order to
organize and understand such large datasets, in recent years, summarization techniques …

Extracting valuable insights from vast amounts of unstructured digital text presents
significant challenges across diverse domains. This research addresses this challenge by …

In the last years, the opinion summarization task has gained much importance because of
the large amount of online information and the increasing interest in learning the user …

The Vector Space Model (VSM), is the main technique to model the semantics from the text.
However, the VSM model suffers from notable limitations. The main alternative model for …

Automatic text summarization is a process of reducing the length of original document
without affecting the content by extracting important information from huge amount of text …

We suggest a new method for the task of extractive text summarization using graph-based
ranking algorithms. The main idea of this paper is to rank Maximal Frequent Sequences …

This chapter describes a real, multi-document, multilingual news summarization application,
named NewSum, the research problems behind it, as well as the novel methods proposed …

In this paper we adapt and explore strategies for generating multi-document summaries
based on relationship maps, which represent texts as graphs (maps) of interrelated …