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summarization [2017/08/13 17:30]
127.0.0.1 external edit
summarization [2018/10/16 20:36]
admin
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 We address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural language story for the album. For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album photos, select representative (summary) photos, and compose the story. Automatic and human evaluations show our model achieves better performance on selection, generation, and retrieval than baselines. We address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural language story for the album. For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album photos, select representative (summary) photos, and compose the story. Automatic and human evaluations show our model achieves better performance on selection, generation, and retrieval than baselines.
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 +https://​arxiv.org/​abs/​1801.10198 Generating Wikipedia by Summarizing Long Sequences
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 +https://​arxiv.org/​abs/​1804.05685v1 A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
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 +https://​arxiv.org/​abs/​1808.10792 Bottom-Up Abstractive Summarization
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 +https://​arxiv.org/​abs/​1810.05739 Unsupervised Neural Multi-document Abstractive Summarization
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