The rapid growth of the online information services causes the problem of information explosion. Automatic text summarization techniques are essential for dealing with this problem. There are different approaches to text summarization and different systems have used one or a combination of them. Considering the wide variety of summarization techniques there should be an evaluation mechanism to ...

Text summarization is the process of extracting salient information from the source text and to present that information to the user in the form of summary. It is very difficult for human beings to manually summarize large documents of text. Automatic abstractive summarization provides the required solution but it is a challenging task because it requires deeper analysis of text. In this paper,...

We show that by making use of information common to document sets belonging to a common category, we can improve the quality of automatically extracted content in multi-document summaries. This simple property is widely applicable in multi-document summarization tasks, and can be encapsulated by the concept of category-specific importance (CSI). Our experiments show that CSI is a valuable metri...

The immediate availability of a vast amount of multimedia content has created a growing need for improvements in the field of content analysis and summarization. While researchers have been rapidly making contributions and improvements to the field, we must never forget that content analysis and summarization themselves are not the user’s goals. Users’ primary interests fall into one of two cat...

We describe iNeATS – an interactive multi-document summarization system that integrates a state-of-the-art summarization engine with an advanced user interface. Three main goals of the system are: (1) provide a user with control over the summarization process, (2) support exploration of the document set with the summary as the staring point, and (3) combine text summaries with alternative prese...

This paper introduces a novel hierarchical summarization approach for automatic multidocument summarization. By creating a hierarchical representation of the words in the input document set, the proposed approach is able to incorporate various objectives of multidocument summarization through an integrated framework. The evaluation is conducted on the DUC 2007 data set.

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...

Information Extraction (IE) and Summarization share the same goal of extracting and presenting the relevant information of a document. While IE was a primary element of early abstractive summarization systems, it's been left out in more recent extractive systems. However, extracting facts, recognizing entities and events should provide useful information to those systems and help resolve semant...

Without a summarization system in source language, we try to generate a summary in source language, using translated documents by a machine translator and a summarization system in target language. For summarizing multiple documents translated by a machine translator, we extract important sentences, and remove redundant sentences using an improved term-weighting method. It assigns weights to wo...

The graph-based ranking models have been widely used for multi-document summarization recently. By utilizing the correlations between sentences, the salient sentences can be extracted according to the ranking scores. However, sentences are treated in a uniform way without considering the topic-level information in traditional methods. This paper proposes the topic-oriented PageRank (ToPageRank)...