The field of machine learning is witnessing its golden era as deep learning slowly becomes
the leader in this domain. Deep learning uses multiple layers to represent the abstractions of …

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 …

Single document summarization is the task of producing a shorter version of a document
while preserving its principal information content. In this paper we conceptualize extractive …

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 …

The core of information retrieval (IR) is to identify relevant information from large-scale
resources and return it as a ranked list to respond to user's information need. In recent years …

We propose a unified model combining the strength of extractive and abstractive
summarization. On the one hand, a simple extractive model can obtain sentence-level …

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

In this work, we propose a novel method for training neural networks to perform single-
document extractive summarization without heuristically-generated extractive labels. We call …

In this paper, we propose SummCoder, a novel methodology for generic extractive text
summarization of single documents. The approach generates a summary according to three …

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 …