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...

We study correlation of rankings of text summarization systems using evaluation methods with and without human models. We apply our comparison framework to various well-established contentbased evaluation measures in text summarization such as coverage, Responsiveness, Pyramids and ROUGE studying their associations in various text summarization tasks including generic and focus-based multi-docu...

This article examines the application of two single-document sentence compression techniques to the problem of multi-document summarization—a “parse-and-trim” approach and a statistical noisy-channel approach. We introduce the Multi-Candidate Reduction (MCR) framework for multi-document summarization, in which many compressed candidates are generated for each source sentence. These candidates a...

coherency—and be able to draw the “best” information from a set of documents. Automatic single-document text summarization1 has been an active research area since the 1950s, with a renaissance of approaches since the 1990s. Human single-document summarization is well defined when guidelines and recommendations drive performance.2,3 System-generated single-document summaries, while not always ma...

This paper describes an efficient algorithm for language independent generic extractive summarization for single document. The algorithm is based on structural and statistical (rather than semantic) factors. Through evaluations performed on a single-document summarization for English, Hindi, Gujarati and Urdu documents, we show that the method performs equally well regardless of the language. T...

Many previous research studies on extractive text summarization consider a subset of words in a document as keywords and use a sentence ranking function that ranks sentences based on their similarities with the list of extracted keywords. But the use of key concepts in automatic text summarization task has received less attention in literature on summarization. The proposed work uses key concep...

Single-document summarization and multidocument summarization are very closely related tasks and they have been widely investigated independently. This paper examines the mutual influences between the two tasks and proposes a novel unified approach to simultaneous single-document and multidocument summarizations. The mutual influences between the two tasks are incorporated into a graph model an...