The search for interesting information in a huge data collection is a tough job frustrating the seekers for that information. The automatic text summarization has come to facilitate such searching process. Automatic text summarization is to compress an original document into an abridged version by extracting almost all of the essential concepts with text mining techniques. The selection of dist...

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...

Automatic summarization can help users extract the most important pieces of information from the vast amount of text digitized into electronic form everyday. Central to automatic summarization is the notion of similarity between sentences in text. In this paper we propose the use of continuous vector representations for semantically aware representations of sentences as a basis for measuring si...

The Text Analysis Conferences (TAC) offer a unique occasion to show innovative approaches to text summarization. As a first incursion into this new research area, LIPN participated in the Update Summarization task of TAC 2008. The LIPN wanted to improve the results obtained during TAC 2008 and to confirm that the changes made to its summarization system really enhanced the quality of the automa...

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...

Text Summarization has been an extensively studied problem. Traditional approaches to text summarization rely heavily on feature engineering. In contrast to this, we propose a fully data-driven approach using feedforward neural networks for single document summarization. We train and evaluate the model on standard DUC 2002 dataset which shows results comparable to the state of the art models. T...