We investigate the consistency of human assessors involved in summarization evaluation to understand its effect on system ranking and automatic evaluation techniques. Using Text Analysis Conference data, we measure annotator consistency based on human scoring of summaries for Responsiveness, Readability, and Pyramid scoring. We identify inconsistencies in the data and measure to what extent the...

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...

This paper presents our work on the evaluation of syntactic based sentence compression for automatic text summarization. Sentence compression techniques can contribute to text summarization by removing redundant and irrelevant information and allowing more space for more relevant content. However, very little work has focused on evaluating the contribution of this idea for summarization. In thi...

Multi-document summarization is the automatic production of a unique summary from a collection of texts. In this paper, we propose a statistical generative approach for multi-document summarization that combines simple information such as sentence position in the text and semantic-discursive information from CST (Cross-Document Structure Theory). In particular, we formulate the multi-document s...

Single-document summarization aims to reduce the size of a text document while preserving the most important information. Much work has been done on open-domain summarization. This paper presents an automatic way to mine domain-specific patterns from text documents. With a small amount of effort required for manual selection, these patterns can be used for domain-specific scenario-based documen...

Motivated by governmental, commercial and academic interests, automatic text summarization area has experienced an increasing number of researches and products, which led to a countless number of summarization methods. In this paper, we present a comprehensive comparative evaluation of the main automatic text summarization methods based on Rhetorical Structure Theory (RST), claimed to be among ...

We present a system which uses lexical chains as an intermediate representation for automatic text summarization. This system builds on previous research by implementing a lexical chain extraction algorithm in linear time. The system is reasonably domain independent and takes as input any text or HTML document. The system outputs a short summary based on the most salient concepts from the origi...

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...

Title of dissertation: STATISTICAL ANALYSIS OF AUTOMATIC SUMMARIZATION EVALUATION Peter A. Rankel, Doctor of Philosophy, 2016 Dissertation directed by: Professor Eric V. Slud Department of Mathematics Dr. John M. Conroy IDA/Center for Computing Sciences This dissertation applies statistical methods to the evaluation of automatic summarization using data from the Text Analysis Conferences in 200...

Today, with digitally stored information available in abundance, even for many minor languages, this information must by some means be filtered and extracted in order to avoid drowning in it. Automatic summarization is one such technique, where a computer summarizes a longer text to a shorter non-rendundant form. Apart from the major languages of the world there are a lot of languages for which...