Multi-document summarization (MDS) is an effective tool for information aggregation that
generates an informative and concise summary from a cluster of topic-related documents …

Sentence scoring and sentence selection are two main steps in extractive document
summarization systems. However, previous works treat them as two separated subtasks. 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 …

We propose a neural multi-document summarization (MDS) system that incorporates
sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation …

Numerous approaches for identifying important content for automatic text summarization
have been developed to date. Topic representation approaches first derive an intermediate …

It has now been 50 years since the publication of Luhn's seminal paper on automatic
summarization. During these years the practical need for automatic summarization has …

The past few years have witnessed increased interest in the potential use of wireless sensor
networks (WSNs) in applications such as disaster management, combat field …

We provide a literature review about Automatic Text Summarization (ATS) systems. We
consider a citation-based approach. We start with some popular and well-known papers that …

Multimodal summarization has drawn much attention due to the rapid growth of multimedia
data. The output of the current multimodal summarization systems is usually represented in …

We present an exploration of generative probabilistic models for multi-document
summarization. Beginning with a simple word frequency based model (Nenkova and …