Although recent years has seen increased and successful research efforts in the areas of single -document summarization, multi-document summarization, and information extraction, very few investigations have explored the potential of merging summarization and information extraction techniques. This paper presents and evaluates the initial version of RIPTIDES, a system that combines information ...

Automatic text summarization, the reduction of a text to its essential content is fundamental for an on-line information society. Although many summarization algorithms exist, there are few tools or infrastructures providing capabilities for developing summarization applications. This paper presents a new version of SUMMA, a text summarization toolkit for the development of adaptive summarizati...

The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of...

Supervised methods for extractive speech summarization require a large training set. Summary annotation is often expensive and time consuming. In this paper, we exploit semi-supervised approaches to leverage unlabeled data. In particular, we investigate co-training for the task of extractive meeting summarization. Compared with text summarization, speech summarization task has its unique charac...

Multi-document summarization is a fundamental tool for document understanding and has received much attention recently. Given a collection of documents, a variety of summarization methods based on different strategies have been proposed to extract the most important sentences from the original documents. However, very few studies have been reported on aggregating different summarization methods...

We present SumBasic+, a powerful multi-document summarization system built from first principles. SumBasic+ is designed as a baseline system to gauge the level of summarization results we could obtain using simple statistical techniques. Our extractive summarization system is based on word frequency statistics similar to the SumBasic method. Nevertheless, we were able to considerably improve it...

With the rapid growth of internet also there is increase in on-line text document. Accessing such huge number of electronic textual documents creates challenge in front of user. It requires user to analyze the searched results one by one until satisfied information is acquired, which is time consuming process. Summary of document helps user to know about the page is about what? There are differ...

This paper examines the summarization of events that evolve through time. It discusses di erent types of evolution taking into account the time in which the incidents of an event are happening and the different sources reporting on the speci c event. It proposes an approach for multi-document summarization which employs \messages" for representing the incidents of an event and cross-document re...

Although there has been much theoretical work on using various information status distinctions to explain the form of references in written text, there have been few studies that attempt to automatically learn these distinctions for generating references in the context of computer-regenerated text. In this article, we present a model for generating references to people in news summaries that in...

Existing work for abstractive multidocument summarization utilise existing phrase structures directly extracted from input documents to generate summary sentences. These methods can suffer from lack of consistence and coherence in merging phrases. We introduce a novel approach for abstractive multidocument summarization through partial dependency tree extraction, recombination and linearization...