Generating textual storyline to improve situation awareness in disaster management

Zhou, Wubai; Shen, Chao; Li, Tao; Chen, Shu‐Ching; Xie, Ning

doi:10.1109/iri.2014.7051942

Cited by 19 publications

(6 citation statements)

References 22 publications

(24 reference statements)

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“…Jiang et al [8] consider the inter-arrival information and proposed a dynamics model for temporal event summarization. Zhou et al [9] introduce a two-layer storyline framework to generate the textual disaster storyline.…”

Section: Related Work a Multi-document Summarizationmentioning

confidence: 99%

“…After selecting the representative events, we are ready to generate the local storyline to reflect the details about how the disaster affects a certain area and we figure out this problem by solving a Steiner Tree problem [9]. Steiner Tree is a subtree from the Multi-view Graph which contains all the representative events with minimum cost.…”

Section: ) Local Storylines Generationmentioning

confidence: 99%

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Generating Multimedia Storyline for Effective Disaster Information Awareness

Yuan

Zhou

2019

IEEE Access

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Storyline generation has emerged to be an effective method to describe the evolution of disaster. However, due to the temporal-spatial, heterogeneous, and information overload, most of the existing storylines are only based on textual data and deliver limited information. In this paper, we introduce a novel framework for generating multimedia storylines to provide more concise and vivid information and deeper understanding of real-time events. We first adopt generative adversarial networks to implement an unsupervised bilingual document summarizing model. Then, we transform image and text incorporation problem into a multi-label learning problem and use convolutional neural networks to train a classification model. And finally, the bilingual documents and images are jointly summarized and embedded into a two-layer storyline generating framework. The experiments on real Hurricane data sets demonstrate the effectiveness of the proposed methods in each level and the overall framework.INDEX TERMS Storyline, disaster management, generative adversarial networks, convolutional neural networks.

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Section: Related Work a Multi-document Summarizationmentioning

confidence: 99%

Section: ) Local Storylines Generationmentioning

confidence: 99%

Generating Multimedia Storyline for Effective Disaster Information Awareness

Yuan

Zhou

2019

IEEE Access

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“…As an application of the techniques discussed earlier, we now introduce disaster storyline-generating systems [Lin et al 2012;Zhou et al 2014] which extract event summarization information from heterogeneous data sources.…”

Section: Disaster Storyline Generationmentioning

confidence: 99%

Data-Driven Techniques in Disaster Information Management

Xie

Zeng

et al. 2017

ACM Comput. Surv.

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Improving disaster management and recovery techniques is one of national priorities given the huge toll caused by man-made and nature calamities. Data-driven disaster management aims at applying advanced data collection and analysis technologies to achieve more effective and responsive disaster management, and has undergone considerable progress in the last decade. However, to the best of our knowledge, there is currently no work that both summarizes recent progress and suggests future directions for this emerging research area. To remedy this situation, we provide a systematic treatment of the recent developments in data-driven disaster management. Specifically, we first present a general overview of the requirements and system architectures of disaster management systems and then summarize state-of-the-art data-driven techniques that have been applied on improving situation awareness as well as in addressing users’ information needs in disaster management. We also discuss and categorize general data-mining and machine-learning techniques in disaster management. Finally, we recommend several research directions for further investigations.

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“…Additionally, unsupervised techniques such as language modeling (Allan et al, 2001) have been used for temporal summarization. In recent years, ranking and graph-based methods (Radev et al, 2004b;Erkan and Radev, 2004;Mihalcea and Tarau, 2004;Fader et al, 2007;Hassan et al, 2008;Mei et al, 2010;Yan et al, 2011b;Yan et al, 2011a;Zhao et al, 2013;Ng et al, 2014;Zhou et al, 2014;Glavaš andŠnajder, 2014;Tran et al, 2015;Dehghani and Asadpour, 2015) have also proved popular for extractive timeline summarization, often in an unsupervised setting. Dynamic programming (Kiernan and Terzi, 2009) and greedy algorithms (Althoff et al, 2015) have also been considered for constructing summaries over time.…”

Section: Related Workmentioning

confidence: 99%

A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization

Wang

Mehdad

Radev³

et al. 2016

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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A key challenge for timeline summarization is to generate a concise, yet complete storyline from large collections of news stories. Previous studies in extractive timeline generation are limited in two ways: first, most prior work focuses on fully-observable ranking models or clustering models with hand-designed features that may not generalize well. Second, most summarization corpora are text-only, which means that text is the sole source of information considered in timeline summarization, and thus, the rich visual content from news images is ignored. To solve these issues, we leverage the success of matrix factorization techniques from recommender systems, and cast the problem as a sentence recommendation task, using a representation learning approach. To augment text-only corpora, for each candidate sentence in a news article, we take advantage of top-ranked relevant images from the Web and model the image using a convolutional neural network architecture. Finally, we propose a scalable low-rank approximation approach for learning joint embeddings of news stories and images. In experiments, we compare our model to various competitive baselines, and demonstrate the stateof-the-art performance of the proposed textbased and multimodal approaches. * This work was performed when William Wang and Dragomir Radev were visiting Yahoo NYC.

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Generating textual storyline to improve situation awareness in disaster management

Cited by 19 publications

References 22 publications

Generating Multimedia Storyline for Effective Disaster Information Awareness

Generating Multimedia Storyline for Effective Disaster Information Awareness

Data-Driven Techniques in Disaster Information Management

A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization

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