Automatic text summarization essentially condenses a long document into a shorter format
while preserving its information content and overall meaning. It is a potential solution to the …

Query-oriented summarization aims at extracting an informative summary from a document
collection for a given query. It is very useful to help users grasp the main information related …

Background Text summarization is a challenging problem in Natural Language Processing,
which involves condensing the content of textual documents without losing their overall …

We present a new method to generate extractive multi-document summaries. The method
uses Integer Linear Programming to jointly maximize the importance of the sentences it …

This paper presents a novel query expansion method, which is combined in the graph-
based algorithm for query-focused multi-document summarization, so as to resolve the …

Most of the text summarization research carried out to date has been concerned with the
summarization of short documents (eg, news stories, technical reports), and very little work if …

Most multi-document summarization systems follow the extractive framework based on
various features. While more and more sophisticated features are designed, the reasonable …

Multi-document summarization aims to create a condensed summary while retaining the
main characteristics of the original set of documents. Under such background, sentence …

Multi‐document summarization is a process of automatic creation of a compressed version
of a given collection of documents that provides useful information to users. In this article we …

The authors address the problem of unsupervised ensemble ranking. Traditional
approaches either combine multiple ranking criteria into a unified representation to obtain …