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Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset [article]

Piji Li, Lidong Bing, Wai Lam
2017 arXiv   pre-print
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

Piji Li, Lidong Bing, Wai Lam
2017 Proceedings of the Workshop on New Frontiers in Summarization  
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]

Congbo Ma, Wei Emma Zhang, Mingyu Guo, Hu Wang, Quan Z. Sheng
2021 arXiv   pre-print
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

Yongjian You, Weijia Jia, Tianyi Liu, Wenmian Yang
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
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

Piji Li, Wai Lam, Lidong Bing, Weiwei Guo, Hang Li
2017 Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing  
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]

Haggai Roitman, Guy Feigenblat, David Konopnicki, Doron Cohen, Odellia Boni
2018 arXiv   pre-print
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

Congbo Ma, Wei Emma Zhang, Mingyu Guo, Hu Wang, QUAN Z. Sheng
2022 ACM Computing Surveys  
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]

Tian Shi, Yaser Keneshloo, Naren Ramakrishnan, Chandan K. Reddy
2020 arXiv   pre-print
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  
., +, 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  
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]

Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan
2020 arXiv   pre-print
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

Arpita Roy, Shimei Pan
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
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

Zakia Jalil, Jamal Abdul Nasir, Muhammad Nasir
2021 IEEE Access  
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

Wenwu Zhu, Xin Wang, Hongzhi Li
2019 IEEE transactions on circuits and systems for video technology (Print)  
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

Gonca Gürses-Tran, Antonello Monti
2022 Forecasting  
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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