In this paper, a novel text clustering technique is proposed to summarize text documents. The clustering method, so called ‘Ensemble Clustering Method’, combines both genetic algorithms (GA) and particle swarm optimization (PSO) efficiently and automatically to get the best clustering results. The summarization with this clustering method is to effectively avoid the redundancy in the summarized...

We present a submodular function-based framework for query-focused opinion summarization. Within our framework, relevance ordering produced by a statistical ranker, and information coverage with respect to topic distribution and diverse viewpoints are both encoded as submodular functions. Dispersion functions are utilized to minimize the redundancy. We are the first to evaluate different metric...

This Ph.D. thesis is the result of several years of research on automatic text summarization. Three major contributions are presented in the form of published and yet to be published papers. They follow a path that moves away from extractive summarization and toward abstractive summarization. The first article describes the HexTac experiment, which was conducted to evaluate the performance of h...

This paper reports an automatic speech summarization method and experimental results using English broadcast news speech. In our proposed method, a set of words maximizing a summarization score indicating an appropriateness of summarization is extracted from automatically transcribed speech. This extraction is performed using a Dynamic Programming (DP) technique according to a target compressio...

Summarization is one of the key features of human intelligence. It plays an important role in understanding and representation. With rapid and continual expansion of texts, pictures and videos in cyberspace, automatic summarization becomes more and more desirable. Text summarization has been studied for over half century, but it is still hard to automatically generate a satisfied summary. Tradi...

In addition to the familiar tasks of update summarization and automatic summary evaluation, the TAC 2010 summarization track includes a new task: guided summarization. It is designed to “encourage a deeper linguistic (semantic) analysis of the source documents” in order to generate a summary guided by a given set of aspects. The new task is an upgrade of query-focused summarization in that an a...

Due to increasing use of internet and online technologies or online data, there is vast increase in the electronic documents. When a data is being retrieved from such a huge collection of electronic documents, hundreds and thousands of documents are retrieved. Hence, for user, it is not possible to read all the retrieved documents. Also, these documents contain redundant information. In such si...

Abstract—Text Categorization is the task of automatically sorting a set of documents into categories from a predefined set and Text Summarization is a brief and accurate representation of input text such that the output covers the most important concepts of the source in a condensed manner. Document Summarization is an emerging technique for understanding the main purpose of any kind of documen...

Over the last few years there has been substantial research on text summarization, but comparatively little research has been carried out on adaptable components that allow rapid development and evaluation of summarization solutions. This paper presents a set of adaptable summarization components together with well-established evaluation tools, all within the GATE paradigm. The toolkit includes...