In the field of artificial intelligence, human–computer interaction (HCI) technology and its
related intelligent robot technologies are essential and interesting contents of research …

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 …

Abstractive summarization is the ultimate goal of document summarization research, but
previously it is less investigated due to the immaturity of text generation techniques …

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

We develop a Ranking framework upon Recursive Neural Networks (R2N2) to rank
sentences for multi-document summarization. It formulates the sentence ranking task as a …

As a framework for extractive summarization, sentence regression has achieved state-of-the-
art performance in several widely-used practical systems. The most challenging task within …

In this paper, three methods of extracting single document summary by combining
supervised learning with unsupervised learning are proposed. The purpose of these three …

Query relevance ranking and sentence saliency ranking are the two main tasks in extractive
query-focused summarization. Previous supervised summarization systems often perform …

Developed so far, multi-document summarization has reached its bottleneck due to the lack
of sufficient training data and diverse categories of documents. Text classification just makes …

Multidocument summarization has gained popularity in many real world applications
because vital information can be extracted within a short time. Extractive summarization …