Existing methods for single document summarization usually make use of only the information contained in the specified document. This paper proposes the technique of document expansion to provide more knowledge to help single document summarization. A specified document is expanded to a small document set by adding a few neighbor documents close to the document, and then the graphranking based ...

Although single-document summarization is a well-studied task, the nature of multidocument summarization is only beginning to be studied in detail. While close attention has been paid to what technologies are necessary when moving from single to multi-document summarization, the properties of humanwritten multi-document summaries have not been quantified. In this paper, we empirically character...

This paper describes a method for language independent extractive summarization that relies on iterative graph-based ranking algorithms. Through evaluations performed on a single-document summarization task for English and Portuguese, we show that the method performs equally well regardless of the language. Moreover, we show how a metasummarizer relying on a layered application of techniques fo...

Due to its promise to alleviate information overload, text summarization has attracted the attention of many researchers. However, it has remained a serious challenge. Here, we first prove empirical limits on the recall (and F1scores) of extractive summarizers on the DUC datasets under ROUGE evaluation for both the single-document and multi-document summarization tasks. Next we define the conce...

The goal of automated summarization is to tackle the “information overload” problem by extracting and perhaps compressing the most important content of a document. Due to the difficulty that singledocument summarization has in beating a standard baseline, especially for news articles, most efforts are currently focused on multi-document summarization. The goal of this study is to reconsider the...

today, with rapid growth of the world wide web and creation of internet sites and online text resources, text summarization issue is highly attended by various researchers. extractive-based text summarization is an important summarization method which is included of selecting the top representative sentences from the input document. when, we are facing into large data volume documents, the extr...

In this paper, we propose a summarization system which automatically classifies type of document set and summarizes a document set with its appropriate summarization mechanism. This system will classify a document set into three types: (a) One topic type, (b) multi-topic type, and (c) others. These types will be identified using information of high frequency nouns and Named Entity. In our multi...

In this paper we present three term weighting approaches for multi-lingual document summarization and give results on the DUC 2002 data as well as on the 2013 Multilingual Wikipedia feature articles data set. We introduce a new intervalbounded nonnegative matrix factorization. We use this new method, latent semantic analysis (LSA), and latent Dirichlet allocation (LDA) to give three term-weight...