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Rushes video summarization using a collaborative approach
This paper describes the video summarization system developed by the partners of the K-Space European Network of Excellence for the TRECVID 2008 BBC rushes summarization evaluation. We propose an original method based on individual content segmentation and selection tools in a collaborative system. Our system is organized in several steps. First, we segment the video, secondly we identify relevant and redundant segments, and finally, we select a subset of segments to concatenate and build the final summary with video acceleration incorporated. We analyze the performance of our system through the TRECVID evaluation
Improving Abstraction in Text Summarization
Abstractive text summarization aims to shorten long text documents into a
human readable form that contains the most important facts from the original
document. However, the level of actual abstraction as measured by novel phrases
that do not appear in the source document remains low in existing approaches.
We propose two techniques to improve the level of abstraction of generated
summaries. First, we decompose the decoder into a contextual network that
retrieves relevant parts of the source document, and a pretrained language
model that incorporates prior knowledge about language generation. Second, we
propose a novelty metric that is optimized directly through policy learning to
encourage the generation of novel phrases. Our model achieves results
comparable to state-of-the-art models, as determined by ROUGE scores and human
evaluations, while achieving a significantly higher level of abstraction as
measured by n-gram overlap with the source document
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