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

Grega, Michał; Smaïli, Kamel; Leszczuk, Mikołaj; González-Gallardo, Carlos-Emiliano; Torres‐Moreno, Juan‐Manuel; Pontes, Elvys Linhares; Fohr, Dominique; Mella, Odile; Menacer, Mohamed Amine; Jouvet, Denis

doi:10.1007/978-3-319-98678-4_42

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…The final tuning needs the number and structure of the layers. We consider up to three fully connected hidden layers with a number of neurons: layer0: [3, 6, 12, 24, 48], layer1: [0, 6,12,24], and layer2: [0, 3,6], where 0 means that this layer is missing. Of course, 0 on layer1 means that layer2 also has the value 0.…”

Section: Resultsmentioning

confidence: 99%

“…Given the use of TRV, the qualitative assessment does not focus on the subject's satisfaction with the quality of the video sequence, but measures how the subject uses TRV to perform specific tasks [1]. These tasks may include: Video surveillance-recognising number plates, telemedicine/remote diagnostics-correct diagnosis, fire safety-fire detection, rear-view cameras-parking the car, gaming-recognising and correctly responding to a virtual enemy, video messaging and report generation-video summarisation [2,3]. In relation to entertainment videos, research has been conducted on the content parameters that most influence perceptual quality.…”

Section: Introductionmentioning

confidence: 99%

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Objective Video Quality Assessment Method for Face Recognition Tasks

et al. 2022

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Nowadays, there are many metrics for overall Quality of Experience (QoE), both those with Full Reference (FR), such as Peak Signal-to-Noise Ratio (PSNR) or Structural Similarity (SSIM), and those with No Reference (NR), such as Video Quality Indicators (VQI), which are successfully used in video processing systems to evaluate videos whose quality is degraded by different processing scenarios. However, they are not suitable for video sequences used for recognition tasks (Target Recognition Videos, TRV). Therefore, correctly estimating the performance of the video processing pipeline in both manual and Computer Vision (CV) recognition tasks is still a major research challenge. There is a need for objective methods to evaluate video quality for recognition tasks. In response to this need, we show in this paper that it is possible to develop the new concept of an objective model for evaluating video quality for face recognition tasks. The model is trained, tested and validated on a representative set of image sequences. The set of degradation scenarios is based on the model of a digital camera and how the luminous flux reflected from the scene eventually becomes a digital image. The resulting degraded images are evaluated using a CV library for face recognition as well as VQI. The measured accuracy of a model, expressed as the value of the F-measure parameter, is 0.87.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Objective Video Quality Assessment Method for Face Recognition Tasks

et al. 2022

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“…When the input and output language is different, the task is seen as cross-lingual summarization, where traditionally machine translation and summarization have been integrated and combined into the same approach, by either first translating the original document into the target language and then summarizing it, or vice versa Shreve (2006), Wan, Li, and Xiao (2010), Grega, Smaïli, Leszczuk, González-Gallardo, Torres-Moreno, Pontes, Fohr, Mella, Menacer, and Jouvet (2018). Zhu et al (2019) propose a different approach, where the task of cross-lingual summarization is addressed end-to-end with the support of automatically constructed corpora.…”

Section: Abstractive Summarizationmentioning

confidence: 99%

Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning

Erdem

Kuyu

Yagcioglu

et al. 2022

jair

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Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.

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“…AMIS is a Chist-Era project, the principal objective is to develop a system, helping people to understand the content of a source video by presenting its main ideas in a target understandable language. This system is based on several components such as: video summarization, audio summarization, text summarization, automatic speech recognition system, machine translation and sentiment analysis [23], [5] and [4]. Four architectures have been proposed, one of these scenarios is given in Fig.1, which corresponds to a pipeline assembly of some of the mentioned Fig.…”

Section: Introductionmentioning

confidence: 99%

A Fine-Grained Multilingual Analysis Based on the Appraisal Theory: Application to Arabic and English Videos

Abidi

Fohr

Jouvet

et al. 2019

Communications in Computer and Information Science

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The objective of this paper is to compare the opinions of two videos in two different languages. To do so, a fine-grained approach inspired from the appraisal theory is used to analyze the content of the videos that concern the same topic. In general, the methods devoted to sentiment analysis concern the study of the polarity of a text or an utterance. The appraisal approach goes further than the basic polarity sentiments and consider more detailed sentiments by covering additional attributes of opinions such as: Attitude, Graduation and Engagement. In order to achieve such a comparison, in AMIS (Chist-Era project), we collected a corpus of 1503 Arabic and 1874 English videos. These videos need to be aligned in order to compare their contents, that is why we propose several methods to make them comparable. Then the best one is selected to align them and to constitute the data-set necessary for the fine-grained sentiment analysis.

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

Cited by 5 publications

References 15 publications

Objective Video Quality Assessment Method for Face Recognition Tasks

Objective Video Quality Assessment Method for Face Recognition Tasks

Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning

A Fine-Grained Multilingual Analysis Based on the Appraisal Theory: Application to Arabic and English Videos

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