Explicit diversification of event aspects for temporal summarization

During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness

SUPER: Towards the Use of Social Sensors for Security Assessments and Proactive Management of Emergencies

Social media statistics during recent disasters (e.g. the 20 million tweets relating to 'Sandy' storm and the sharing of related photos in Instagram at a rate of 10/sec) suggest that the understanding and management of real-world events by civil protection and law enforcement agencies could benefit from the effective blending of social media information into their resilience processes. In this paper, we argue that despite the widespread use of social media in various domains (e.g. marketing/branding/finance), there is still no easy, standardized and effective way to leverage different social media streams -- also referred to as social sensors -- in security/emergency management applications. We also describe the EU FP7 project SUPER (Social sensors for secUrity assessments and Proactive EmeRgencies management), started in 2014, which aims to tackle this technology gap

Whoʼs Watching Us at Work? Toward a Structural-Perceptual Model of Electronic Monitoring and Surveillance in Organizations

Nearly 80% of organizations now employ some form of employee surveillance. This significant level of use infers a salient need for additional theory and research into the effects of monitoring and surveillance. Accordingly, this essay examines the panoptic effects of electronic monitoring and surveillance (EM/S) of social communication in the workplace and the underlying structural and perceptual elements that lead to these effects. It also provides future scholarly perspectives for studying EM/S and privacy in the organization from the vantage point of contemporary communication technologies, such as the telephone, voice mail, e-mail, and instant messaging, utilized for organizational communication. Finally, four propositions are presented in conjunction with a new communication-based model of EM/S, providing a framework incorporating three key components of the panoptic effect: (a) communication technology use, (b) organizational factors, and (c) organizational policies for EM/S

A Comparison of Nuggets and Clusters for Evaluating Timeline Summaries

There is growing interest in systems that generate timeline summaries by filtering high-volume streams of documents to retain only those that are relevant to a particular event or topic. Continued advances in algorithms and techniques for this task depend on standardized and reproducible evaluation methodologies for comparing systems. However, timeline summary evaluation is still in its infancy, with competing methodologies currently being explored in international evaluation forums such as TREC. One area of active exploration is how to explicitly represent the units of information that should appear in a 'good' summary. Currently, there are two main approaches, one based on identifying nuggets in an external 'ground truth', and the other based on clustering system outputs. In this paper, by building test collections that have both nugget and cluster annotations, we are able to compare these two approaches. Specifically, we address questions related to evaluation effort, differences in the final evaluation products, and correlations between scores and rankings generated by both approaches. We summarize advantages and disadvantages of nuggets and clusters to offer recommendations for future system evaluation

Taxonomy of the Simulium perflavum species-group (Diptera: Simuliidae) : with description of a new species from Brazil

The larva, pupa, male, and female of Simulium trombetense n. sp. are described and illustrated. This species was collected in the Brazilian Amazon region in the states of Amapa, Amazonas, Para, and Roraima near the edges ofthe crystalline basement-rock formation ofthe Pre-Cambrian Guiana Shield. Keys for larvae, pupae, males, and females of species in the Simulium perflauum Species-Group are presented, as well as new observations on adult, pupal, and larval characters. Evidence is given to support the species status of S. maroniense Floch and Abonnenc, previously considered synonymous with S. rorotaense Floch and Abonnenc

Social fragmentation, deprivation and urbanicity: relation to first-admission rates for psychoses

<i>Declaration</i> <i>of</i> <i>interest</i>: None. <i>Background</i>: Social disorganisation, fragmentation and isolation have long been posited as influencing the rate of psychoses at area level. Measuring such societal constructsis difficult. A census-based index measuring social fragmentation has been proposed. <i>Aims</i>: To investigate the association between first-admission rates for psychosis and area-based measures of social fragmentation, deprivation and urban/rural index. <i>Method</i>: We used indirect standardisation methods and logistic regression models to examine associations of social fragmentation, deprivation and urban/rural categories with first admissions for psychoses in Scotland for the 5-year period 1989–1993. <i>Results</i>: Areas characterised by high social fragmentation had higher first-ever admission rates for psychosis independent of deprivation and urban/rural status. There was a dose–response relationship between social fragmentation category and first-ever admission rates for psychosis. There was no statistically significant interaction between social fragmentation, deprivation and urban/rural index. <i>Conclusions</i>: First-admission rates are strongly associated with measures of social fragmentation, independent of material deprivation and urban/rural category

Analyzing Disproportionate Reaction via Comparative Multilingual Targeted Sentiment in Twitter

Global events such as terrorist attacks are commented upon in social media, such as Twitter, in different languages and from different parts of the world. Most prior studies have focused on monolingual sentiment analysis, and therefore excluded an extensive proportion of the Twitter userbase. In this paper, we perform a multilingual comparative sentiment analysis study on the terrorist attack in Paris, during November 2015. In particular, we look at targeted sentiment, investigating opinions on specific entities, not simply the general sentiment of each tweet. Given the potentially inflammatory and polarizing effect that these types of tweets may have on attitudes, we examine the sentiments expressed about different targets and explore whether disproportionate reaction was expressed about such targets across different languages. Specifically, we assess whether the sentiment for French speaking Twitter users during the Paris attack differs from English-speaking ones. We identify disproportionately negative attitudes in the English dataset over the French one towards some entities and, via a crowdsourcing experiment, illustrate that this also extends to forming an annotator bias

A Study of Realtime Summarization Metrics

Unexpected news events, such as natural disasters or other human tragedies, create a large volume of dynamic text data from official news media as well as less formal social media. Automatic real-time text summarization has become an important tool for quickly transforming this overabundance of text into clear, useful information for end-users including affected individuals, crisis responders, and interested third parties. Despite the importance of real-time summarization systems, their evaluation is not well understood as classic methods for text summarization are inappropriate for real-time and streaming conditions. The TREC 2013-2015 Temporal Summarization (TREC-TS) track was one of the first evaluation campaigns to tackle the challenges of real-time summarization evaluation, introducing new metrics, ground-truth generation methodology and dataset. In this paper, we present a study of TREC-TS track evaluation methodology, with the aim of documenting its design, analyzing its effectiveness, as well as identifying improvements and best practices for the evaluation of temporal summarization systems

On the Reproducibility and Generalisation of the Linear Transformation of Word Embeddings

Linear transformation is a way to learn a linear relationship between two word embeddings, such that words in the two different embedding spaces can be semantically related. In this paper, we examine the reproducibility and generalisation of the linear transformation of word embeddings. Linear transformation is particularly useful when translating word embedding models in different languages, since it can capture the semantic relationships between two models. We first reproduce two linear transformation approaches, a recent one using orthogonal transformation and the original one using simple matrix transformation. Previous findings on a machine translation task are re-examined, validating that linear transformation is indeed an effective way to transform word embedding models in different languages. In particular, we show that the orthogonal transformation can better relate the different embedding models. Following the verification of previous findings, we then study the generalisation of linear transformation in a multi-language Twitter election classification task. We observe that the orthogonal transformation outperforms the matrix transformation. In particular, it significantly outperforms the random classifier by at least 10% under the F1 metric across English and Spanish datasets. In addition, we also provide best practices when using linear transformation for multi-language Twitter election classification