Text summarization endeavors to produce a summary version of a text, while maintaining the original ideas. The textual content on the web, in particular, is growing at an exponential rate. The ability to decipher through such massive amount of data, in order to extract the useful information, is a major undertaking and requires an automatic mechanism to aid with the extant repository of informa...

Summarization is a Process of filtering the most important information from source/sources for a particular user and task. Summarization is a very useful task which gives support to many other tasks. It takes advantage of the techniques developed for Natural Language Processing tasks. Multidocument summarization is a technique of summarize the multiple document into one paragraph. Multi-documen...

Hierarchical summarization technique summarizes a large document based on the hierarchical structure and salient features of the document. Previous study has shown that hierarchical summarization is a promising technique which can effectively extract the most important information from the source document. Hierarchical summarization has been extended to summarization of multiple documents. Thre...

Text summarization is the process of taking a text document and creating a compressed version that consists of the most useful information for the user. One distinguishes between single-document summarizers (SDS) and multi-document summarizers (MDS). Multi-document summarization is much more complicated than single-document summarization. Factors that make multi-document summarization more diff...

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...

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 ...

Multi-document summarization aims at delivering the majority of information content from multiple documents using much less lengthy texts, usually a short paragraph of several hundred words. This paper surveys several different approaches to multi-document summarization by first building a unified high level view of the multi-document summarization problem, and then comparing different approach...

Applying document clustering techniques to multidocument summarization is a challenging problem, mostly because of the redundancy that exists in multiple sources. We compare several document clustering techniques for multi-document summarization in the NTCIR-4 TSC test collection. We conducted an experiment to evaluate the effectiveness of reducing redundancy in the production of summaries. Fro...

Abstract The availability of large-scale datasets has driven the development neural models that create generic summaries for single or multiple documents. For query-focused summarization (QFS), labeled training data in form queries, documents, and is not readily available. We provide a unified modeling framework any kind summarization, under assumption all are response to query, which observed ...