A Multilingual Study of Compressive Cross-Language Text Summarization

Cross-Language Text Summarization (CLTS) generates summaries in a language different from the language of the source documents. Recent methods use information from both languages to generate summaries with the most informative sentences. However, these methods have performance that can vary according to languages, which can reduce the quality of summaries. In this paper, we propose a compressive framework to generate cross-language summaries. In order to analyze performance and especially stability, we tested our system and extractive baselines on a dataset available in four languages (English, French, Portuguese, and Spanish) to generate English and French summaries. An automatic evaluation showed that our method outperformed extractive state-of-art CLTS methods with better and more stable ROUGE scores for all languages

Automatic Text Summarization with a Reduced Vocabulary Using Continuous Space Vectors

poster paperInternational audienceIn 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

Studying the influence of adding lexical-semantic knowledge to Principal Component Analysis technique for multilingual summarization

El objetivo de la generación automática de resúmenes es reducir la dimensión de un texto y a su vez mantener la información relevante del mismo. En este artículo se analiza y aplica la técnica de Análisis de Componentes Principales, que es independiente del idioma, para la generación de resúmenes extractivos mono-documento y multilingües. Dicha técnica se estudiará con el objetivo de poder evaluar su funcionamiento cuando se incorpora (o no) conocimiento léxico-semántico, a partir del uso de recursos y herramientas dependientes del idioma. La experimentación propuesta se ha realizado en base a dos corpus de diferente naturaleza: noticias periodísticas y artículos de la Wikipedia en tres idiomas (alemán, español e inglés) para verificar el uso de esta técnica en varios escenarios. Los enfoques propuestos presentan resultados muy competitivos comparados con generadores de resúmenes multilingües existentes, lo que indica que, aunque exista un claro margen de mejora respecto a la técnica y el tipo de conocimiento incorporado, ésta tiene una gran potencial para ser aplicada en otros contextos e idiomas.The objective of automatic text summarization is to reduce the dimension of a text keeping the relevant information. In this paper we analyse and apply the language-independent Principal Component Analysis technique for generating extractive single-document multilingual summaries. This technique will be studied to evaluate its performance with and without adding lexical-semantic knowledge through language-dependent resources and tools. Experiments were conducted using two different corpora: newswire and Wikipedia articles in three languages (English, German and Spanish) to validate the use of this technique in several scenarios. The proposed approaches show very competitive results compared to multilingual available systems, indicating that, although there is still room for improvement with respect to the technique and the type of knowledge to be taken into consideration, this has great potential for being applied in other contexts and for other languages.Esta investigación se ha realizado gracias a la financiación recibida en los proyectos: DIIM2.0: Desarrollo de técnicas Inteligentes e Interactivas de Minería y generación de información sobre la web 2.0 (PROMETEOII/2014/001) de la Generalitat Valenciana; SAM (FP7-611312) de la Comisión Europea; “Análisis de Tendencias Mediante Técnicas de Opinión Semántica” (TIN2012-38536-C03-03) y “Técnicas de Deconstrucción en la Tecnologías del Lenguaje Humano” (TIN2012-31224)), del Ministerio de Economía y Competitividad del Gobierno de España; “Explotación y tratamiento de la información disponible en Internet para la anotación y generación de textos adaptados al usuario” (GRE13-15), de la Universidad de Alicante

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

long paperInternational audienceCross-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 sum-marization 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

Arabic multi-document text summarisation

Multi-document summarisation is the process of producing a single summary of a collection of related documents. Much of the current work on multi-document text summarisation is concerned with the English language; relevant resources are numerous and readily available. These resources include human generated (gold-standard) and automatic summaries. Arabic multi-document summarisation is still in its infancy. One of the obstacles to progress is the limited availability of Arabic resources to support this research. When we started our research there were no publicly available Arabic multi-document gold-standard summaries, which are needed to automatically evaluate system generated summaries. The Document Understanding Conference (DUC) and Text Analysis Conference (TAC) at that time provided resources such as gold-standard extractive and abstractive summaries (both human and system generated) that were only available in English. Our aim was to push forward the state-of-the-art in Arabic multi-document summarisation. This required advancements in at least two areas. The first area was the creation of Arabic test collections. The second area was concerned with the actual summarisation process to find methods that improve the quality of Arabic summaries. To address both points we created single and multi-document Arabic test collections both automatically and manually using a commonly used English dataset and by having human participants. We developed extractive language dependent and language independent single and multi-document summarisers, both for Arabic and English. In our work we provided state-of-the-art approaches for Arabic multi-document summarisation. We succeeded in including Arabic in one of the leading summarisation conferences the Text Analysis Conference (TAC). Researchers on Arabic multi-document summarisation now have resources and tools that can be used to advance the research in this field