In this paper we, present (1) an unsupervised system for AESOP task and (2) a generic multi-document summarization system for guided summarization task. We propose the use of: (1) the role and importance of words and sentences in document and, (2) number and coverage strength of topics in document for both AESOP and Guided summarization task. We also use some other statistical features, simple ...

Summarization, an extremely important technique in Data Mining is an automatic learning technique aimed to extract the most valuable information from a large size document or the articles. The goal is to create the summary of the document, but substantially different from each other. Text Document summarization refers to the summarization of text documents based upon their content. The proposed...

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...

We show that by making use of information common to document sets belonging to a common category, we can improve the quality of automatically extracted content in multi-document summaries. This simple property is widely applicable in multi-document summarization tasks, and can be encapsulated by the concept of category-specific importance (CSI). Our experiments show that CSI is a valuable metri...

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...

We participated in three multi-document summarization tasks at the DUC-2003 formal run and evaluated the performance of our summarization system. Our summarization system based on sentence extraction also incorporated a module to estimate similarity between sentences for multi-document summarization. The similarity information was used for selecting the representative sentence among similar sen...

Multi-document summarization is a fundamental tool for understanding documents. Given a collection of documents, most of existing multidocument summarization methods automatically generate a static summary for all the users using unsupervised learning techniques such as sentence ranking and clustering. However, these methods almost exclude human from the summarization process. They do not allow...

Summarization, an extremely important technique in Data Mining is an automatic learning technique aimed to extract the most valuable information from a large size document or the articles. The goal is to create the summary of the document, but substantially different from each other. Text Document summarization refers to the summarization of text documents based upon their content. The proposed...

Headline generation is a task of abstractive text summarization, and previously suffers from the immaturity of natural language generation techniques. Recent success of neural sentence summarization models shows the capacity of generating informative, fluent headlines conditioned on selected recapitulative sentences. In this paper, we investigate the extension of sentence summarization models t...