@inproceedings{3fca79b2c24d4ff181581c21dc4d9404,
title = "Quality-Diversity Summarization with Unsupervised Autoencoders",
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.",
keywords = "Autoencoders, DPPs, Summarization",
author = "Lei Li and Zuying Huang and Natalia Vanetik and Marina Litvak",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 28th International Conference on Artificial Neural Networks, ICANN 2019 ; Conference date: 17-09-2019 Through 19-09-2019",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-30490-4_24",
language = "English",
isbn = "9783030304898",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "293--299",
editor = "Tetko, {Igor V.} and Pavel Karpov and Fabian Theis and Vera Kurkov{\'a}",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2019",
address = "Germany",
}