Abstract
This paper introduces a novel perspective on unlabeled data driven technology for extractive summarization. Because unsupervised autoencoders, combined with neural network language models, help to capture deep semantic features for sentence quality, we propose to integrate autoencoders with sampling method based on Determinantal point processes (DPPs) [1] to extract diverse sentences with high qualities, and generate brief summaries. The unique fusion of unsupervised autoencoders and DPPs sampling has never been adopted before. We illustrate the advantages of this attempt against statistics based approaches through experiments in multilingual environment for single-document and multi-document summarization tasks. Our algorithms evaluated with ROUGE F-measure [2] obtain better scores in several varieties of languages on MMS-2015 dataset and MSS-2015 dataset.
This work was supported in part by the Beijing Municipal Commission of Science and Technology under Grant Z181100001018035; National Social Science Foundation of China under Grant 16ZDA055; National Natural Science Foundation of China under Grant 91546121; Engineering Research Center of Information Networks, Ministry of Education.
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Li, L., Huang, Z., Vanetik, N., Litvak, M. (2019). Quality-Diversity Summarization with Unsupervised Autoencoders. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_24
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DOI: https://doi.org/10.1007/978-3-030-30490-4_24
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