In this paper we present the usage of singular value decomposition (SVD) in text summarization. Firstly, we mention the taxonomy of generic text summarization methods. Then we describe principles of the SVD and its possibilities to identify semantically important parts of a text. We propose a modification of the SVD-based summarization, which improves the quality of generated extracts. In the s...

Researchers in automatic document summarization have already adopted many techniques from existing machine translation literature. Likewise, there is much that the machine translation community can learn from current research in summarization. Automatic Summarization, by Inderjeet Mani, provides a firm grounding in the primary techniques that have been applied to the summarization task, so that...

This dissertation proposes a new automatic speech summarization method through word extraction. In this method, a set of words maximizing a summarization score indicating an appropriateness of summarization is extracted from automatically transcribed speech. This extraction is performed according to a target compression ratio using a dynamic programming technique sentence by sentence. The extra...

Proper evaluation is crucial for developing high-quality computerized text summarization systems. In the clinical domain, the specialized information needs of the clinicians complicates the task of evaluating automatically produced clinical text summaries. In this paper we present and compare the results from both manual and automatic evaluation of computer-generated summaries. These are compos...

We report evaluation results for our summarization system and analyze the resulting summarization data for three different types of corpora. To develop a robust summarization system, we have created a system based on sentence extraction and applied it to summarize Japanese and English newspaper articles, obtained some of the top results at two evaluation workshops. We have also created sentence...

This study proposes a novel semantic graph embedding-based abstractive text summarization technique for the Arabic language, namely SemG-TS. SemG-TS employs deep neural network to produce summary. A set of experiments were conducted evaluate performance and compare results those popular baseline word embedding called word2vec. new dataset was collected experiments. Two evaluation methodologies ...

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 deals with our past and recent research in text summarization. We went from single-document summarization through multidocument summarization to update summarization. We describe the development of our summarizer which is based on latent semantic analysis (LSA). The classical LSA-based summarization model was improved by Iterative Residual Rescaling. We propose the update summarizati...

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...

The goals of my dissertation are: 1) to propose a French terminology for the presentation of evaluation results of automatic summaries, 2) to identify and describe experimental variables in evaluations of automatic summaries, 3) to highlight the most common tendencies, inconsistencies and methodological problems in summarization evaluation experiments, and 4) to make recommendations for the pre...