Suchergebnisse

Evaluation is crucial in the research and development of automatic summarization applications, in order to determine the appropriateness of a summary based on different criteria, such as the content it contains, and the way it is presented. To perform an adequate evaluation is of great relevance to ensure that automatic summaries can be useful for the context and/or application they are generated for. To this end, researchers must be aware of the evaluation metrics, approaches, and datasets that are available, in order to decide which of them would be the most suitable to use, or to be able to propose new ones, overcoming the possible limitations that existing methods may present. In this article, a critical and historical analysis of evaluation metrics, methods, and datasets for automatic summarization systems is presented, where the strengths and weaknesses of evaluation efforts are discussed and the major challenges to solve are identified. Therefore, a clear up-to-date overview of the evolution and progress of summarization evaluation is provided, giving the reader useful insights into the past, present and latest trends in the automatic evaluation of summaries. ; This research is partially funded by the European Commission under the Seventh (FP7 - 2007- 2013) Framework Programme for Research and Technological Development through the SAM (FP7-611312) project; by the Spanish Government through the projects VoxPopuli (TIN2013-47090-C3-1-P) and Vemodalen (TIN2015-71785-R), the Generalitat Valenciana through project DIIM2.0 (PROMETEOII/2014/001), and the Universidad Nacional de Educación a Distancia through the project "Modelado y síntesis automática de opiniones de usuario en redes sociales" (2014-001-UNED-PROY).

This paper presents a detailed analysis of the use of crowdsourcing services for the Text Summarization task in the context of the tourist domain. In particular, our aim is to retrieve relevant information about a place or an object pictured in an image in order to provide a short summary which will be of great help for a tourist. For tackling this task, we proposed a broad set of experiments using crowdsourcing services that could be useful as a reference for others who want to rely also on crowdsourcing. From the analysis carried out through our experimental setup and the results obtained, we can conclude that although crowdsourcing services were not good to simply gather gold-standard summaries (i.e., from the results obtained for experiments 1, 2 and 4), the encouraging results obtained in the third and sixth experiments motivate us to strongly believe that they can be successfully employed for finding some patterns of behaviour humans have when generating summaries, and for validating and checking other tasks. Furthermore, this analysis serves as a guideline for the types of experiments that might or might not work when using crowdsourcing in the context of text summarization. ; This work was supported by the EU-funded TRIPOD project (IST-FP6-045335) and by the Spanish Government through the FPU program and the projects TIN2009-14659-C03-01, TSI 020312-2009-44, and TIN2009-13391-C04-01; and by Conselleria d'Educació–Generalitat Valenciana (grant no. PROMETEO/2009/119 and grant no. ACOMP/2010/286).

This article aims at assessing the effects of the Federal Reserve's quantitative easing (QE) programmes on both economic activity and prices in the United States. Using a structural vector autoregression (SVAR) model on monthly data from January 2007 to March 2017, it is assumed that a substantial fraction of the liquidity injected under the Federal Reserve's quantitative easing programmes was used to artificially inflate stock prices. Furthermore, QE is assumed to be a competitive devaluation programme. The findings reveal that QE helps support economic activity, while its effect on inflation is rather small and insignificant. Besides, it is also found that QE boosts stock prices but does not have a significant effect on the US dollar.