TSum4act: A Framework for Retrieving and Summarizing Actionable Tweets During a Disaster for Reaction

Nguyen, Minh-Tien; Kitamoto, Asanobu; Nguyen, Tri-Thanh

doi:10.1007/978-3-319-18032-8_6

Cited by 25 publications

(12 citation statements)

References 7 publications

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“…In recent times, researchers tried to extract and summarize situational information from Twitter [16], [17], [18], [19]. Nguyen et al [6] extracted subjects, named entities, events, numerals from tweets, developed a graph among tweets, generated clusters of related tweets, and finally applied PageRank based iterative update scheme within the tweets present in each cluster to get rank of the tweets (TSum4act). A greedy strategy to track real-time events was proposed by Osborne et al [20].…”

Section: Related Workmentioning

confidence: 99%

“…To get a quick overview of the event and what tweeters are saying about it, a summary of these tweets is very valuable. Several recent studies [1], [3], [6] tried to summarize the information posted during crisis. However, all of these methods tried to select informative tweets based on some criteria to represent summary at a particular instant (extractive summarization).…”

Section: Introductionmentioning

confidence: 99%

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Summarizing Situational Tweets in Crisis Scenarios: An Extractive-Abstractive Approach

Rudra

Goyal

Ganguly

et al. 2019

IEEE Trans. Comput. Soc. Syst.

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Microblogging platforms such as Twitter are widely used by eyewitnesses and affected people to post situational updates during mass convergence events such as natural and man-made disasters. These crisis-related messages disperse among multiple classes/categories such as infrastructure damage, shelter needs, information about missing, injured, and dead people etc. Side by side, we observe that sometimes people post information about their missing relatives, friends with details like name, last location etc. Such kind of information is time-critical in nature and their pace and quantity do not match with other kind of generic situational updates. Also, requirement of different stakeholders (government, NGOs, rescue workers etc.) vary a lot. This brings two-fold challenges -(i). extracting important high-level situational updates from these messages, assign them appropriate categories, finally summarize big trove of information in each category and (ii). extracting small-scale time-critical sparse updates related to missing or trapped persons. In this paper, we propose a classification-summarization framework which first assigns tweets into different situational classes and then summarizes those tweets. In the summarization phase, we propose a two stage extractive-abstractive summarization framework. In the first step, it extracts a set of important tweets from the whole set of information, develops a bigram-based word-graph from those tweets, and generates paths by traversing the word-graph. Next, it uses an Integer-linear programming (ILP) based optimization technique to select the most important tweets and paths based on different optimization parameters such as informativeness, coverage of content words etc. Apart from general class-wise summarization, we also show the customization of our summarization model to address time-critical sparse information needs (e.g., missing relatives). Our proposed method is time and memory efficient and shows better performance than state-of-the-art methods both in terms of quantitative and qualitative judgement.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Summarizing Situational Tweets in Crisis Scenarios: An Extractive-Abstractive Approach

Rudra

Goyal

Ganguly

et al. 2019

IEEE Trans. Comput. Soc. Syst.

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show abstract

“…Evaluation was carried out on 58,000 tweets for 20 events and the system can fill such event schemas with an F-measure of 60%. TSum4act (Nguyen et al, 2015) was designed for disaster responses based on tweets and has been evaluated on a dataset containing 230,535 tweets. Anantharam et al (Anantharam et al, 2014) focused on extracting city events by solving a sequence labeling problem.…”

Section: Related Workmentioning

confidence: 99%

Event extraction from Twitter using Non-Parametric Bayesian Mixture Model with Word Embeddings

Zhou

Zhang

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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To extract structured representations of newsworthy events from Twitter, unsupervised models typically assume that tweets involving the same named entities and expressed using similar words are likely to belong to the same event. Hence, they group tweets into clusters based on the cooccurrence patterns of named entities and topical keywords. However, there are two main limitations. First, they require the number of events to be known beforehand, which is not realistic in practical applications. Second, they don't recognise that the same named entity might be referred to by multiple mentions and tweets using different mentions would be wrongly assigned to different events. To overcome these limitations, we propose a nonparametric Bayesian mixture model with word embeddings for event extraction, in which the number of events can be inferred automatically and the issue of lexical variations for the same named entity can be dealt with properly. Our model has been evaluated on three datasets with sizes ranging between 2,499 and over 60 million tweets. Experimental results show that our model outperforms the baseline approach on all datasets by 5-8% in F-measure.

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“…Imran et al (2013) call these 'information nuggets' and include locations, times, objects and numbers. Nguyen et al (2015) are able to summarise tweets containing actionable information that would answer the questions 'what? ', 'where?'…”

Section: Crisis Taxonomies Ontologies and Entity Extractionmentioning

confidence: 99%

Creating corroborated crisis reports from social media data through formal concept analysis

et al. 2016

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During a crisis citizens reach for their smart phones to report, comment and explore information surrounding the crisis. These actions often involve social media and this data forms a large repository of real-time, crisis related information. Law enforcement agencies and other first responders see this information as having untapped potential. That is, it has the capacity extend their situational awareness beyond the scope of a usual command and control centre. Despite this potential, the sheer volume, the speed at which it arrives, and unstructured nature of social media means that making sense of this data is not a trivial task and one that is not yet satisfactorily solved; both in crisis management and beyond. Therefore we propose a multi-stage process to extract meaning from this data that will provide relevant and near real-time information to command and control to assist in decision support. This process begins with the capture of real-time social media data, the development of specific LEA and crisis focused taxonomies for categorisation and entity extraction, the application of formal concept analysis for aggregation and corroboration and the presentation of this data via map-based and other visualisations. We demonstrate that this novel use of formal concept analysis in combination with context-based entity extraction has the potential to inform law enforcement and/or humanitarian responders about on-going crisis events using social media data in the context of the 2015 Nepal earthquake.

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TSum4act: A Framework for Retrieving and Summarizing Actionable Tweets During a Disaster for Reaction

Cited by 25 publications

References 7 publications

Summarizing Situational Tweets in Crisis Scenarios: An Extractive-Abstractive Approach

Summarizing Situational Tweets in Crisis Scenarios: An Extractive-Abstractive Approach

Event extraction from Twitter using Non-Parametric Bayesian Mixture Model with Word Embeddings

Creating corroborated crisis reports from social media data through formal concept analysis

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