Text summarization is a subtask of natural language processing referring to the automatic
creation of a concise and fluent summary that captures the main ideas and topics from one …

The task of automatic document summarization aims at generating short summaries for
originally long documents. A good summary should cover the most important information of …

Automatic generation of summaries from multiple news articles is a valuable tool as the
number of online publications grows rapidly. Single document summarization (SDS) …

Over-dependence on domain ontology and lack of knowledge sharing across domains are
two practical and yet less studied problems of dialogue state tracking. Existing approaches …

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

Generating a text abstract from a set of documents remains a challenging task. The neural
encoder-decoder framework has recently been exploited to summarize single documents …

The increase in online text generation by humans and machines needs automatic text
summarization systems. Recent research studies commonly use deep learning, besides …

The textual similarity is a crucial aspect for many extractive text summarization methods. A
bag-of-words representation does not allow to grasp the semantic relationships between …

Generating an abstract from a collection of documents is a desirable capability for many real-
world applications. However, abstractive approaches to multi-document summarization have …

We provide an analysis of current evaluation methodologies applied to summarization
metrics and identify the following areas of concern:(1) movement away from evaluation by …