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Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset
[article]
2017
arXiv
pre-print
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To tackle RA-MDS, we extend a variational auto-encodes (VAEs) based MDS framework by jointly considering news documents and reader comments. ...
We investigate the problem of reader-aware multi-document summarization (RA-MDS) and introduce a new dataset for this problem. ...
Left: Latent semantic modeling via variation auto-encoders for news sentence x d and comment sentence x c . Middle: Comment sentence weight estimation. ...
arXiv:1708.01065v1
fatcat:lcp2lfjzu5b67fknv77jbqrexa
Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset
2017
Proceedings of the Workshop on New Frontiers in Summarization
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To tackle RA-MDS, we extend a variational auto-encodes (VAEs) based MDS framework by jointly considering news documents and reader comments. ...
We investigate the problem of readeraware multi-document summarization (RA-MDS) and introduce a new dataset for this problem. ...
Left: Latent semantic modeling via variation auto-encoders for news sentence x d and comment sentence x c . Middle: Comment sentence weight estimation. Right:
Table 1 : 1 Summarization performance. ...
doi:10.18653/v1/w17-4512
dblp:conf/emnlp/LiBL17
fatcat:alulkb6oljd2dn2ylgd4ws22eu
Multi-document Summarization via Deep Learning Techniques: A Survey
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2011.04843v2 fatcat:lwdpyj64m5ei7aagyfcu24a5z4Other Versions
Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents. ...
We propose a novel taxonomy to summarize the design strategies of neural networks and conduct a comprehensive summary of the state-of-the-art. ...
The subsequent work [70] also follows the paradigm of variational auto-encoder framework for unsupervised multi-document summarization task. ...
arXiv:2011.04843v3
fatcat:zfi52xxef5g2tjkaw6hgjpwa5i
Improving Abstractive Document Summarization with Salient Information Modeling
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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To tackle the above difficulties, we propose a Transformerbased encoder-decoder framework with two novel extensions for abstractive document summarization. ...
Comprehensive document encoding and salient information selection are two major difficulties for generating summaries with adequate salient information. ...
Li et al. (2017) creatively incorporate the variational auto-encoder into the seq2seq model to learn the latent structure information. ...
doi:10.18653/v1/p19-1205
dblp:conf/acl/YouJLY19
fatcat:35gv7n7axnfcvgcbsajqskbuka
Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization
2017
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
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Inspired by this observation, we propose a cascaded attention based unsupervised model to estimate the salience information from the text for compressive multi-document summarization. ...
When people recall and digest what they have read for writing summaries, the important content is more likely to attract their attention. ...
conducted salience estimation jointly considering reconstructions on several different vector spaces generated by a variational auto-ecoder framework. ...
doi:10.18653/v1/d17-1221
dblp:conf/emnlp/LiLBGL17
fatcat:liis25rfrfcyvn4372ybikfw3u
Unsupervised Dual-Cascade Learning with Pseudo-Feedback Distillation for Query-based Extractive Summarization
[article]
2018
arXiv
pre-print
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We propose Dual-CES -- a novel unsupervised, query-focused, multi-document extractive summarizer. Dual-CES is designed to better handle the tradeoff between saliency and focus in summarization. ...
Dual-CES is even shown to be able to outperform strong supervised summarizers. ...
Others have used various sparse coding techniques for selecting a subset of sentences that minimizes a given documents reconstruction error [12, 26, 16, 9, 11] or used a variational auto-encoder for ...
arXiv:1811.00436v1
fatcat:7hmsm37cybafhetflrl5htp4m4
Multi-document Summarization via Deep Learning Techniques: A Survey
2022
ACM Computing Surveys
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Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents. ...
We propose a novel taxonomy to summarize the design strategies of neural networks and conduct a comprehensive summary of the state-of-the-art. ...
We also summarize nine network design strategies based on our extensive studies of the current models. • We discuss the open issues of deep learning based multi-document summarization and identify the ...
doi:10.1145/3529754
fatcat:r4lngnzrgjbfziazokpd2c5s44
Neural Abstractive Text Summarization with Sequence-to-Sequence Models
[article]
2020
arXiv
pre-print
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As part of this survey, we also develop an open source library, namely, Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization. ...
Several models were first proposed for language modeling and generation tasks, such as machine translation, and later applied to abstractive text summarization. ...
Incorporating variational auto-encoders (VAEs) [66, 116] into the encoder-decoder framework provides a practical solution for this problem. ...
arXiv:1812.02303v4
fatcat:m2wsnnquivhrrmv7eoyfcciswy
2020 Index IEEE Transactions on Multimedia Vol. 22
2020
IEEE transactions on multimedia
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., +, TMM Feb. 2020 459-473 Energy Compaction-Based Image Compression Using Convolutional Auto-Encoder. ...
., +, TMM
March 2020 744-759
Similarity-Aware and Variational Deep Adversarial Learning for Robust
Facial Age Estimation. ...
Image watermarking Blind Watermarking for 3-D Printed Objects by Locally Modifying Layer Thickness. 2780 -2791 Low-Light Image Enhancement With Semi-Decoupled Decomposition. ...
doi:10.1109/tmm.2020.3047236
fatcat:llha6qbaandfvkhrzpe5gek6mq
2021 Index IEEE Transactions on Multimedia Vol. 23
2021
IEEE transactions on multimedia
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
Keighrey, C., +, TMM 2021 Transferable Knowledge-Based Multi-Granularity Fusion Network for Adversarial 3D Convolutional Auto-Encoder for Abnormal Event Detection in Videos. ...
., +, TMM 2021 4515-4525 Adversarial 3D Convolutional Auto-Encoder for Abnormal Event Detection in Videos. ...
doi:10.1109/tmm.2022.3141947
fatcat:lil2nf3vd5ehbfgtslulu7y3lq
From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information
[article]
2020
arXiv
pre-print
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Instead, there is much manifold information to be summarized, such as the summary for a web page based on a query in the search engine, extreme long document (e.g., academic paper), dialog history and ...
Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words. ...
Acknowledgements We would like to thank the anonymous reviewers for their constructive comments. ...
arXiv:2005.04684v1
fatcat:35ub2qoaezdq7fw7ptbvrbj37i
Incorporating Extra Knowledge to Enhance Word Embedding
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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important for different NLP applications. ...
In this survey, we summarize the recent advances in incorporating extra knowledge to enhance word embedding. ...
Acknowledgments We would like to thank the anonymous reviewers for their constructive comments. ...
doi:10.24963/ijcai.2020/676
dblp:conf/ijcai/GaoCR0020
fatcat:n3hj4lad2vcphpmzdnwgflp7x4
Extractive Multi-Document Summarization: A Review of Progress in the Last Decade
2021
IEEE Access
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Unsupervised deep auto-encoder was trained such that Recurrent Neural Networks (RNNs) encoder with Gated Recurrent Units (GRUs) and RNN decoder with conditional GRUs.
E. ...
First, text clustering is used on the documents to create clusters via K-means so that similar documents contribute to the summarization task. ...
doi:10.1109/access.2021.3112496
fatcat:6alsaxs7knfwhmue2ygdhq3hwy
Multi-modal Deep Analysis for Multimedia
2019
IEEE transactions on circuits and systems for video technology (Print)
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2019 pre-print arXiv:1910.04964v1 fatcat:76yma7ca4fhajduyuyuos3csziOther Versions
answering, multi-modal video summarization, multi-modal visual pattern mining and multi-modal recommendation. ...
In this article, we present a deep and comprehensive overview for multi-modal analysis in multimedia. ...
ACKNOWLEDGMENT We thank Guohao Li, Shengze Yu and Yitian Yuan for providing relevant materials and valuable opinions. This work will never be accomplished without their useful suggestions. ...
doi:10.1109/tcsvt.2019.2940647
fatcat:l4tchrkgrnaeradvc4nhfan2w4
Advances in Time Series Forecasting Development for Power Systems' Operation with MLOps
2022
Forecasting
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For instance, near real-time capacity procurement takes place in the wholesale market, which is subject to complex interrelations of system operators' grid activities and balancing parties' scheduling ...
Thus, we generate load forecasts by means of a data-driven based forecasting tool ProLoaF, which we benchmark with state-of-the-art baseline models and the auto-machine learning models auto.arima and Facebook ...
Acknowledgments: The authors gratefully acknowledge E.ON Energidistribution for providing the initial forecasting problem formulation and requirements for the demonstration in the H2020 CoordiNet project ...
doi:10.3390/forecast4020028
fatcat:decn7nj3nzhkfgleokckjo52cu
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