Automatic Text Summarization Approaches to Speed up Topic Model Learning Process

The number of documents available into Internet moves each day up. For this reason, processing this amount of information effectively and expressibly becomes a major concern for companies and scientists. Methods that represent a textual document by a topic representation are widely used in Information Retrieval (IR) to process big data such as Wikipedia articles. One of the main difficulty in using topic model on huge data collection is related to the material resources (CPU time and memory) required for model estimate. To deal with this issue, we propose to build topic spaces from summarized documents. In this paper, we present a study of topic space representation in the context of big data. The topic space representation behavior is analyzed on different languages. Experiments show that topic spaces estimated from text summaries are as relevant as those estimated from the complete documents. The real advantage of such an approach is the processing time gain: we showed that the processing time can be drastically reduced using summarized documents (more than 60\% in general). This study finally points out the differences between thematic representations of documents depending on the targeted languages such as English or latin languages.Comment: 16 pages, 4 tables, 8 figure

Audio Summarization with Audio Features and Probability Distribution Divergence

International audienceThe automatic summarization of multimedia sources is an important task that facilitates the understanding of an individual by condensing the source while maintaining relevant information. In this paper we focus on audio summarization based on audio features and the probability of distribution divergence. Our method, based on an extractive summarization approach, aims to select the most relevant segments until a time threshold is reached. It takes into account the segment's length, position and informativeness value. Informativeness of each segment is obtained by mapping a set of audio features issued from its Mel-frequency Cepstral Coefficients and their corresponding Jensen-Shannon divergence score. Results over a multi-evaluator scheme shows that our approach provides understandable and informative summaries

Calculating the Upper Bounds for Portuguese Automatic Text Summarization Using Genetic Algorithm

Over the last years, Automatic Text Summarization (ATS) has been considered as one of the main tasks in Natural Language Processing (NLP) that generates summaries in several languages (e.g., English, Portuguese, Spanish, etc.). One of the most significant advances in ATS is developed for Portuguese reflected with the proposals of various state-of-art methods. It is essential to know the performance of different state-of-the-art methods with respect to the upper bounds (Topline), lower bounds (Baseline-random), and other heuristics (Base-line-first). In recent works, the significance and upper bounds for Single-Docu-ment Summarization (SDS) and Multi-Document Summarization (MDS) using corpora from Document Understanding Conferences (DUC) were calculated. In this paper, a calculus of upper bounds for SDS in Portuguese using Genetic Al-gorithms (GA) is performed. Moreover, we present a comparison of some state-of-the-art methods with respect to the upper bounds, lower bounds, and heuristics to determinate their level of significance

ASHuR: Evaluation of the Relation Summary-Content Without Human Reference Using ROUGE

In written documents, the summary is a brief description of important aspects of a text. The degree of similarity between the summary and the content of a document provides reliability about the summary. Some efforts have been done in order to automate the evaluation of a summary. ROUGE metrics can automatically evaluate a summary, but it needs a model summary built by humans. The goal of this study is to find a quantitative relation between an article content and its summary using ROUGE tests without a model summary built by humans. This work proposes a method for automatic text summarization to evaluate a summary (ASHuR) based on extraction of sentences. ASHuR extracts the best sentences of an article based on the frequency of concepts, cue-words, title words, and sentence length. Extracted sentences constitute the essence of the article; these sentences construct the model summary. We performed two experiments to assess the reliability of ASHuR. The first experiment compared ASHuR against similar approaches based on sentences extraction; the experiment placed ASHuR in the first place in each applied test. The second experiment compared ASHuR against human-made summaries, which yielded a Pearson correlation value of 0.86. Assessments made to ASHuR show reliability to evaluate summaries written by users in collaborative sites (e.g. Wikipedia) or to review texts generated by students in online learning systems (e.g. Moodle)

Summarizing videos into a target language: Methodology, architectures and evaluation

International audienceThe aim of the work is to report the results of the Chist-Era project AMIS (Access Multilingual Information opinionS). The purpose of AMIS is to answer the following question: How to make the information in a foreign language accessible for everyone? This issue is not limited to translate a source video into a target language video since the objective is to provide only the main idea of an Arabic video in English. This objective necessitates developing research in several areas that are not, all arrived at a maturity state: Video summarization, Speech recognition, Machine translation, Audio summarization and Speech segmentation. In this article we present several possible architectures to achieve our objective, yet we focus on only one of them. The scientific locks are be presented, and we explain how to deal with them. One of the big challenges of this work is to conceive a way to evaluate objectively a system composed of several components knowing that each of them has its limits and can propagate errors through the first component. Also, a subjective evaluation procedure is proposed in which several annotators have been mobilized to test the quality of the achieved summaries

Calculating the Upper Bounds for Multi-Document Summarization using Genetic Algorithms

Over the last years, several Multi-Document Summarization (MDS) methods have been presented in Document Understanding Conference (DUC), workshops. Since DUC01, several methods have been presented in approximately 268 publications of the stateof-the-art, that have allowed the continuous improvement of MDS, however in most works the upper bounds were unknowns. Recently, some works have been focused to calculate the best sentence combinations of a set of documents and in previous works we have been calculated the significance for single-document summarization task in DUC01 and DUC02 datasets. However, for MDS task has not performed an analysis of significance to rank the best multi-document summarization methods. In this paper, we describe a Genetic Algorithm-based method for calculating the best sentence combinations of DUC01 and DUC02 datasets in MDS through a Meta-document representation. Moreover, we have calculated three heuristics mentioned in several works of state-of-the-art to rank the most recent MDS methods, through the calculus of upper bounds and lower bounds

Ground Truth Spanish Automatic Extractive Text Summarization Bounds

The textual information has accelerated growth in the most spoken languages by native Internet users, such as Chinese, Spanish, English, Arabic, Hindi, Portuguese, Bengali, Russian, among others. It is necessary to innovate the methods of Automatic Text Summarization (ATS) that can extract essential information without reading the entire text. The most competent methods are Extractive ATS (EATS) that extract essential parts of the document (sentences, phrases, or paragraphs) to compose a summary. During the last 60 years of research of EATS, the creation of standard corpus with human-generated summaries and evaluation methods which are highly correlated with human judgments help to increase the number of new state-of-the-art methods. However, these methods are mainly supported for the English language, leaving aside other equally important languages such as Spanish, which is the second most spoken language by natives and the third most used on the Internet. A standard corpus for Spanish EATS (SAETS) is created to evaluate the state-of-the-art methods and systems for the Spanish language. The main contribution consists of a proposal for configuration and evaluation of 5 state-ofthe-art methods, five systems and four heuristics using three evaluation methods (ROUGE, ROUGE-C, and Jensen-Shannon divergence). It is the first time that Jensen-Shannon divergence is used to evaluate AETS. In this paper the ground truth bounds for the Spanish language are presented, which are the heuristics baseline:first, baseline:random, topline and concordance. In addition, the ranking of 30 evaluation tests of the state-of-the-art methods and systems is calculated that forms a benchmark for SAETS

Features in extractive supervised single-document summarization: case of Persian news

Text summarization has been one of the most challenging areas of research in NLP. Much effort has been made to overcome this challenge by using either abstractive or extractive methods. Extractive methods are preferable due to their simplicity compared with the more elaborate abstractive methods. In extractive supervised single document approaches, the system will not generate sentences. Instead, via supervised learning, it learns how to score sentences within the document based on some textual features and subsequently selects those with the highest rank. Therefore, the core objective is ranking, which enormously depends on the document structure and context. These dependencies have been unnoticed by many state-of-the-art solutions. In this work, document-related features such as topic and relative length are integrated into the vectors of every sentence to enhance the quality of summaries. Our experiment results show that the system takes contextual and structural patterns into account, which will increase the precision of the learned model. Consequently, our method will produce more comprehensive and concise summaries