Add to ORKGIn this paper, we propose to enhance the process of automatic extractive multi-document text summarization by taking into account cross-document structural relationships as posited in Cross-document Structure Theory (CST). An arbitrary multi-document extract can be CST-enhanced by replacing low-salience sentences with other sentences that increase the total number of CST relationships included in the summary. We show that CST-enhanced summaries outperform their unmod-ified counterparts using the relative utility evaluation metric. We also show that the effect of a CST relationship on an ex-tract depends on its type
We propose and develop a simple and efficient algorithm for generating extractive multi-document sum...
Graph-based methods have been developed for multi-document summarization in recent years and they ma...
In this paper, we explore the use of automatic syntactic simplification for improving content select...
This paper discusses a text extraction approach to multi-document summarization that builds on singl...
U.S.A. This paper discusses a text extraction approach to multi-document summarization that builds o...
This paper discusses an sentence extraction approach to multi-document summarization that builds on ...
The world we live today witnesses a fast moving information age due to the ever increasing informati...
This paper discusses passage extraction approaches to multi-document summarization that use availabl...
Due to the tremendous amount of data available today, extracting essential information from such a l...
The popularization of social networks and digital documents has quickly increased the multilingual i...
Abstract. This paper presents a methodology for summarization from multiple documents which are abou...
Automatic summarization is the process of presenting the contents of written documents in a short, c...
By synthesizing information common to retrieved docu-ments, multi-document summarization can help us...
Abstract. In this work we investigate the use of graphs for multi-document summarization. We adapt t...
We show that by making use of information common to document sets belonging to a common category, we...
We propose and develop a simple and efficient algorithm for generating extractive multi-document sum...
Graph-based methods have been developed for multi-document summarization in recent years and they ma...
In this paper, we explore the use of automatic syntactic simplification for improving content select...
This paper discusses a text extraction approach to multi-document summarization that builds on singl...
U.S.A. This paper discusses a text extraction approach to multi-document summarization that builds o...
This paper discusses an sentence extraction approach to multi-document summarization that builds on ...
The world we live today witnesses a fast moving information age due to the ever increasing informati...
This paper discusses passage extraction approaches to multi-document summarization that use availabl...
Due to the tremendous amount of data available today, extracting essential information from such a l...
The popularization of social networks and digital documents has quickly increased the multilingual i...
Abstract. This paper presents a methodology for summarization from multiple documents which are abou...
Automatic summarization is the process of presenting the contents of written documents in a short, c...
By synthesizing information common to retrieved docu-ments, multi-document summarization can help us...
Abstract. In this work we investigate the use of graphs for multi-document summarization. We adapt t...
We show that by making use of information common to document sets belonging to a common category, we...
We propose and develop a simple and efficient algorithm for generating extractive multi-document sum...
Graph-based methods have been developed for multi-document summarization in recent years and they ma...
In this paper, we explore the use of automatic syntactic simplification for improving content select...