Deep Submodular Networks for Extractive Data Summarization

• URL: http://arxiv.org/abs/2010.08593v1
• Date: Fri, 16 Oct 2020 19:06:15 GMT
• Title: Deep Submodular Networks for Extractive Data Summarization
• Authors: Suraj Kothawade, Jiten Girdhar, Chandrashekhar Lavania, Rishabh Iyer
• Abstract summary: We propose an end-to-end learning framework for summarization problems. The Deep Submodular Networks (DSN) framework can be used to learn features appropriate for summarization from scratch. In particular, we show that DSNs outperform simple mixture models using off the shelf features.
• Score: 0.46898263272139784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Models are increasingly becoming prevalent in summarization problems (e.g. document, video and images) due to their ability to learn complex feature interactions and representations. However, they do not model characteristics such as diversity, representation, and coverage, which are also very important for summarization tasks. On the other hand, submodular functions naturally model these characteristics because of their diminishing returns property. Most approaches for modelling and learning submodular functions rely on very simple models, such as weighted mixtures of submodular functions. Unfortunately, these models only learn the relative importance of the different submodular functions (such as diversity, representation or importance), but cannot learn more complex feature representations, which are often required for state-of-the-art performance. We propose Deep Submodular Networks (DSN), an end-to-end learning framework that facilitates the learning of more complex features and richer functions, crafted for better modelling of all aspects of summarization. The DSN framework can be used to learn features appropriate for summarization from scratch. We demonstrate the utility of DSNs on both generic and query focused image-collection summarization, and show significant improvement over the state-of-the-art. In particular, we show that DSNs outperform simple mixture models using off the shelf features. Secondly, we also show that just using four submodular functions in a DSN with end-to-end learning performs comparably to the state-of-the-art mixture model with a hand-crafted set of 594 components and outperforms other methods for image collection summarization.

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