Elvys Linhares Pontes

Cross-Language Text Summarization Using Sentence and Multi-Sentence Compression

Cross-Language Automatic Text Summarization produces a summary in a language different from the language of the source documents. In this paper, we propose a French-to-English cross-lingual summarization framework that analyzes the information in both languages to identify the most relevant sentences. In order to generate more informative cross-lingual summaries, we introduce the use of chunks and two compression methods at the sentence and multi-sentence levels. Experimental results on the MultiLing 2011 dataset show that our framework improves the results obtained by state-of-the art approaches according to ROUGE metrics.

A Second Life for TIIARA: From Bilingual to Multilingual!

Multilingual controlled vocabularies are rare and often very limited in the choice of languages offered. TIIARA (Taxonomy for Image Indexing and RetrievAl) is a bilingual taxonomy developed for image indexing and retrieval. This controlled vocabulary offers indexers and image searchers innovative and coherent access points for ordinary images. The preliminary steps of the elaboration of the bilingual structure are presented. For its initial development, TIIARA included only two languages, French and English. As a logical follow-up, TIIARA was translated into eight languages—Arabic, Spanish, Brazilian Portuguese, Mandarin Chinese, Italian, German, Hindi and Russian—in order to increase its international scope. This paper briefly describes the different stages of the development of the bilingual structure. The processes used in the translations are subsequently presented, as well as the main difficulties encountered by the translators. Adding more languages in TIIARA constitutes an added value for a controlled vocabulary meant to be used by image searchers, who are often limited by their lack of knowledge of multiple languages.

Microblog Contextualization: Advantages and Limitations of a Multi-sentence Compression Approach

The content analysis task of the MC2 CLEF 2017 lab aims to generate small summaries in four languages to contextualize microblogs. This paper analyzes the challenges of this task and also details the advantages and limitations of our approach using a cross-lingual compressive text summarization. We split this task in several subtasks in order to discuss their setup. In addition, we suggest an evaluation protocol to reduce the bias of the current metrics toward the approaches by extraction.

A First Summarization System of a Video in a Target Language

In this paper, we present the first results of the project AMIS (Access Multilingual Information opinionS) funded by Chist-Era. The main goal of this project is to understand the content of a video in a foreign language. In this work, we consider the understanding process, such as the aptitude to capture the most important ideas contained in a media expressed in a foreign language. In other words, the understanding will be approached by the global meaning of the content of a support and not by the meaning of each fragment of a video.

An Integrated AMIS Prototype for Automated Summarization and Translation of Newscasts and Reports

In this paper we present the results of the integration works on the system designed for automated summarization and translation of newscast and reports. We show the proposed system architectures and list the available software modules. Thanks to well defined interfaces the software modules may be used as building blocks allowing easy experimentation with different summarization scenarios.

Automatic Text Summarization with a Reduced Vocabulary Using Continuous Space Vectors

In this paper, we propose a new method that uses continuous vectors to map words to a reduced vocabulary, in the context of Automatic Text Summarization (ATS). This method is evaluated on the MultiLing corpus by the ROUGE evaluation measures with four ATS systems. Our experiments show that the reduced vocabulary improves the performance of state-of-the-art systems.