Multi-document summarization represents the information in a concise and comprehensive manner. In this paper we discuss the significance of ordering of sentences in multi document summarization. We show experimental results on DUC2002 dataset. These results show the ordering of summaries before and, improvement in this, after applying sentence ordering. For this purpose we used a term frequency...

In this paper, we propose a text summarization system for a single document and multiple documents. The system for a single one extracts sentences from a document and itemizes them to generate a summary. We applied this mechanism for Task A (single document summarization). We also utilized this mechanism for multi-document summarization (Task B) except for itemization mechanism. The system for ...

In the era of information overload, text summarization can be defined as the process of extracting useful information from a large space of available content using traditional filtering methods. One of the major challenges in the domain of extraction based summarization is that a single statistical measure is not sufficient to produce efficient summaries which would be close to human-made ‘gold...

Summarization is a way to represent same information in concise way with equal sense. This can be categorized in two type Abstractive and Extractive type. Our work is focused around Extractive summarization. A generic approach to extractive summarization is to consider sentence as an entity, score each sentence based on some indicative features to ascertain the quality of sentence for inclusion...

After lying dormant for over two decades, automated text summarization has experienced a tremendous resurgence of interest in the past few years. Research is being conducted in China, Europe, Japan, and North America, and industry has brought to market more than 30 summarization systems; most recently, a series of large-scale text summarization evaluations, Document Understanding Conference (DU...

Ontology Summarization has been found useful to facilitate ontology engineering tasks in a number of different ways. Recently, it has been recognised as a means to facilitate ontology understanding and then support tasks like ontology reuse in ontology construction. Among the works in literature, not only distinctive methods are used to summarize ontology, also different measures are deployed t...

Clustering is an essential data mining task with various types of applications. Traditional clustering algorithms are based on a vector space model representation. A relational database system often contains multirelational information spread across multiple relations (tables). In order to cluster such data, one would require to restrict the analysis to a single representation, or to construct ...

Automatic text summarization consists of automatically creating a summary of one or more texts. As for Web pages, unfortunately classical techniques cannot be applied in presence of dynamic contents. In this paper, we propose the adoption of snippets –i.e., page excerpts provided together with user query results by search engines– as a text summarization technique. The study is conducted along ...

Library summarization is an effective way to accelerate the analysis of client code. However, information about the client is unknown at the library summarization, preventing complete summarization of the library. An existing approach utilizes tree-adjoining languages (TALs) to provide conditional summaries, enabling the summarization of a library under certain premises. However, the use of TAL...