The acquiring of sentence similarity has become a crucial step in graph-based multi-document summarization algorithms which have been intensively studied during the past decade. Previous algorithms generally considered sentence-level structure information and semantic similarity separately, which, consequently, had no access to grab similarity information comprehensively. In this paper, we present a general framework to exemplify how to combine the two factors above together so as to derive a corpus-oriented and more discriminative sentence similarity. Experimental results on the DUC2004 dataset demonstrate that our approaches could improve the multi-document summarization performance to a considerable extent. ? 2012 Springer-Verlag.EI
Automatic multi-document summarization needs to find representative sentences not only by sentence d...
We present a new approach for summarizing clusters of documents on the same event, some of which are...
Professionals who have to peruse documents in a limited amount of time or private individuals who wa...
Automatic summarization is the process of presenting the contents of written documents in a short, c...
Automatic summarization is the process of presenting the contents of written documents in a short, c...
The technology of summarizing documents automatically is increasing rapidly and may give an answer f...
In recent years, graph-based models and ranking algorithms have drawn considerable attention from th...
By synthesizing information common to retrieved docu-ments, multi-document summarization can help us...
Multi-document summarization aims to create a compressed summary while retaining the main characteri...
Although there has been a great deal of research on automatic summarization, most methods are based ...
Although there has been a great deal of research on au-tomatic summarization, most methods are based...
This paper discusses an sentence extraction approach to multi-document summarization that builds on ...
We present a new approach for summarizing clusters of documents on the same event, some of which a...
We present a new composite similarity metric that combines information from multiple linguistic indi...
Graph-based methods have been developed for multi-document summarization in recent years and they ma...
Automatic multi-document summarization needs to find representative sentences not only by sentence d...
We present a new approach for summarizing clusters of documents on the same event, some of which are...
Professionals who have to peruse documents in a limited amount of time or private individuals who wa...
Automatic summarization is the process of presenting the contents of written documents in a short, c...
Automatic summarization is the process of presenting the contents of written documents in a short, c...
The technology of summarizing documents automatically is increasing rapidly and may give an answer f...
In recent years, graph-based models and ranking algorithms have drawn considerable attention from th...
By synthesizing information common to retrieved docu-ments, multi-document summarization can help us...
Multi-document summarization aims to create a compressed summary while retaining the main characteri...
Although there has been a great deal of research on automatic summarization, most methods are based ...
Although there has been a great deal of research on au-tomatic summarization, most methods are based...
This paper discusses an sentence extraction approach to multi-document summarization that builds on ...
We present a new approach for summarizing clusters of documents on the same event, some of which a...
We present a new composite similarity metric that combines information from multiple linguistic indi...
Graph-based methods have been developed for multi-document summarization in recent years and they ma...
Automatic multi-document summarization needs to find representative sentences not only by sentence d...
We present a new approach for summarizing clusters of documents on the same event, some of which are...
Professionals who have to peruse documents in a limited amount of time or private individuals who wa...