A querybased summary is intended to retrieve a summary of information from a document of a given query. It is useful to help users understand the main information biases of queries. Typically, queries lack semantic i...
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ISBN:
(纸本)9781538621653
A querybased summary is intended to retrieve a summary of information from a document of a given query. It is useful to help users understand the main information biases of queries. Typically, queries lack semantic information, so the querybased summary effect is not ideal. In this paper, we propose a new method based on theme background knowledge to solve problems. In particular, by adding several search engines search query subject related documents to build theme background knowledge, and then the PageRank algorithm is applied to the specified document contains background knowledge and information. The experimental results demonstrate the effectiveness of the proposed method.
The popularity of online video-sharing platforms has fuelled demand for systems that can quickly browse, extract, and summarise video information. Nowadays, numerous automatic multi-video summarization (MVS) technique...
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The popularity of online video-sharing platforms has fuelled demand for systems that can quickly browse, extract, and summarise video information. Nowadays, numerous automatic multi-video summarization (MVS) techniques have come into existence. The existing MVS approach, on the other hand, produces summarised video with a lot of unimportant and duplicate frames. It also arranges frames in a meaningless manner. To solve these problems in MVS, query-based Deep African Vulture Learning (QDAVOL) is proposed in this paper. It uses tag information and web images searched by the query as important information to identify the query intent. An event-based object detection and grouping (EODG) technique is used to assign keyframes to groups of specific events relevant with the query. In addition, we introduce the African vulture optimization algorithm (AVOA) for the efficient key frame selection. Moreover, we have also developed a similarity-based frame closeness (SFC) technique to provide more comprehensible summary. Experimental results demonstrate that the proposed framework outperforms existing approaches in terms of precision (0.765), recall (0.845), and average F-score (0.774) on MVS1K dataset.
The rapid advances in video storage, processing and streaming services, improvements of cellular communication speed, enhancement of mobile phone cameras and increase in social media engagement led to explosive growth...
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The rapid advances in video storage, processing and streaming services, improvements of cellular communication speed, enhancement of mobile phone cameras and increase in social media engagement led to explosive growth in the number of videos generated every minute. Therefore, content-based video searching, browsing, and information retrieval technologies have received significant attention in recent years adapting to the massive number of videos generated. Video summarization techniques are among methodologies which can help users browse the video fast and retrieve information more efficiently by either solely extracting key-frames/segments or assembling the important segments further as video skims, highlights or summaries. In this research, the current video summarization pipeline, collected datasets, and related evaluation metrics are reviewed. Furthermore, various video summarization models which rely on the fusion of video title and visual features using attention networks will be proposed and evaluated using publicly available datasets: 1. A baseline video summarization model which uses correlation among visual features of video frames using attention network is studied. The training procedure and evaluation metrics will be compared against similar recent studies. 2. Extracting Video Title embeddings using pre-trained language models, various methodologies for integrating video title information in the baseline model are studied and evaluated. Re-shaping self-attention to cross-attention, a model which takes advantage of correlation among video title and frame visual features is proposed. Given that the correlation of visual frames in long sequences does not necessarily provide video storyline, the fusion of title information in the proposed model improved the video summarization performance as expected. 3. Finally, to further improve the performance of the proposed model, loss function is modified to combine the accuracy of frame-level score predictions and s
In today's world of managing multimedia content, dealing with the amount of CCTV footage poses challenges related to storage, accessibility and efficient navigation. To tackle these issues, we suggest an encompass...
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In today's world of managing multimedia content, dealing with the amount of CCTV footage poses challenges related to storage, accessibility and efficient navigation. To tackle these issues, we suggest an encompassing technique, for summarizing videos that merges machine-learning techniques with user engagement. Our methodology consists of two phases, each bringing improvements to video summarization. In Phase I we introduce a method for summarizing videos based on keyframe detection and behavioral analysis. By utilizing technologies like YOLOv5 for object recognition, Deep SORT for object tracking, and Single Shot Detector (SSD) for creating video summaries. In Phase II we present a User Interest based Video summarization system driven by machine learning. By incorporating user preferences into the summarization process we enhance techniques with personalized content curation. Leveraging tools such as NLTK, OpenCV, TensorFlow, and the EfficientDET model enables our system to generate customized video summaries tailored to preferences. This innovative approach not only enhances user interactions but also efficiently handles the overwhelming amount of video data on digital platforms. By combining these two methodologies we make progress in applying machine learning techniques while offering a solution to the complex challenges presented by managing multimedia data.
The task of query-Focused Multi-Document summarization is intended to improve agreement in content among human-generated model summaries. query-focus also aids the automated summarizers in directing the summary at spe...
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The task of query-Focused Multi-Document summarization is intended to improve agreement in content among human-generated model summaries. query-focus also aids the automated summarizers in directing the summary at specific topics, which may result in better agreement with these model summaries. This paper explores terms weighting method for query based summarization. It considers the term weight, combines it to score important of sentences, and then it selects the summary sentences and orders them. Experimental result shows that the proposed summarization method is effective.
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