An abstractive Arabic text summarizer with user controlled granularity

Automated summaries help tackle the ever growing volume of information floating around. There are two broad categories: extract and abstract. In the former we retain the more important sentences more or less in their original structure, while the latter requires a fusion of multiple sentences and/or paraphrasing. This is a more challenging task than extract summaries. In this paper, we present a novel generic abstract summarizer for a single document in Arabic language. The system starts by segmenting the input text topic wise. Then, each textual segment is extractively summarized. Finally, we apply rule-based sentence reduction technique. The RST-based extractive summarizer is an enhanced version of the system in Azmi and Al-Thanyyan (2012). By controlling the size of the extract summary of each segment we can cap the size of the final abstractive summary. Both summarizers, the enhanced extractive and the abstractive, were evaluated. We tested our enhanced extractive summarizer on the same dataset in the aforementioned paper, using the measures recall, precision and Rouge. The results show noticeable improvement in the performance, specially the precision in shorter summaries. The abstractive summarizer was tested on a set of 150 documents, generating summaries of sizes 50%, 40%, 30% and 20% (of the original’s word count). The results were assessed by two human experts who graded them out of a maximum score of 5. The average score ranged between 4.53 and 1.92 for summaries at different granularities, with shorter summaries receiving the lower score. The experimental results are encouraging and demonstrate the effectiveness of our approach.     

Knowledge empowered prominent aspect extraction from product reviews

Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects. Conventionally, the prominent review aspects of a product type are determined manually. This costly approach cannot scale to large and cross-domain services such as Amazon.com, Taobao.com or Yelp.com where there are a large number of product types and new products emerge almost everyday. In this paper, we propose a novel method empowered by knowledge sources such as Probase and WordNet, for extracting the most prominent aspects of a given product type from textual reviews. The proposed method, ExtRA (Extraction of Prominent Review Aspects), (i) extracts the aspect candidates from text reviews based on a data-driven approach, (ii) builds an aspect graph utilizing the Probase to narrow the aspect space, (iii) separates the space into reasonable aspect clusters by employing a set ofproposed algorithms and finally (iv) generates K most prominent aspect terms or phrases which do not overlap semantically automatically without supervision from those aspect clusters. ExtRA extracts high-quality prominent aspects as well as aspect clusters with little semantic overlap by exploring knowledge sources. ExtRA can extract not only words but also phrases as prominent aspects. Furthermore, it is general-purpose and can be applied to almost any type of product and service. Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.     

Emerging research topics detection with multiple machine learning models

Emerging research topic detection can benefit the research foundations and policy-makers. With the long-term and recent interest in detecting emerging research topics, various approaches are proposed in the literature. Though, there is still a lack of well-established linkages between the clear conceptual definition of emerging research topics and the proposed indicators for operationalization. This work follows the definition by Wang (2018), and several machine learning models are together used to detect and foresight the emerging research topics. Finally, experimental results on gene editing dataset discover three emerging research topics, which make clear that it is feasible to identify emerging research topics with our framework.     

Human resources for Big Data professions: A systematic classification of job roles and required skill sets

The rapid expansion of Big Data Analytics is forcing companies to rethink their Human Resource (HR) needs. However, at the same time, it is unclear which types of job roles and skills constitute this area. To this end, this study pursues to drive clarity across the heterogeneous nature of skills required in Big Data professions, by analyzing a large amount of real-world job posts published online. More precisely we: 1) identify four Big Data ‘job families’; 2) recognize nine homogeneous groups of Big Data skills (skill sets) that are being demanded by companies; 3) characterize each job family with the appropriate level of competence required within each Big Data skill set. We propose a novel, semi-automated, fully replicable, analytical methodology based on a combination of machine learning algorithms and expert judgement. Our analysis leverages a significant amount of online job posts, obtained through web scraping, to generate an intelligible classification of job roles and skill sets. The results can support business leaders and HR managers in establishing clear strategies for the acquisition and the development of the right skills needed to leverage Big Data at best. Moreover, the structured classification of job families and skill sets will help establish a common dictionary to be used by HR recruiters and education providers, so that supply and demand can more effectively meet in the job marketplace.     

基于“Topic”的语义信息组织技术研究——以DITA和Topic Maps为中心

基于“Topic”的语义信息组织与图书馆学的经典Subject理论密不可分,可将其视为较为抽象化的Subject理论在语义Web环境下的一种“演化”和实用化的主题技术。文章首先解读Subject和Topic的概念,并概述两种基于Topic的国际标准技术,即TopicMaps和DITA;其次,简要描述TopicMaps的技术和应用,并重点介绍在图书馆界鲜为人知的DITA技术及其应用;再次,对基于“Topic”的语义信息组织方法予以总结。     

基于页面内容和链接结构的超链接主题预测算法

主要介绍了设计开发Web主题信息采集系统的一个核心算法——超链接主题预测算法。文章在已有理论的基础上，通过实验分析，发现超链接的主题主要取决于三个因素：父网页的主题相关度、锚文本的主题相关度和Web子图的链接结构特性，从而提出了基于Web页面内容和链接结构的超链接主题预测算法，系统评价结果显示该算法有很好的效果。     

基于引文内容分析的高被引论文主题识别研究

基于被引次数的引文分析无法直接揭示论文的研究内容,利用关键词或从标题、摘要和全文中抽取的主题词很难客观反映论文的被引原因。本文以碳纳米管纤维研究领域的高被引论文为研究对象进行引文内容抽取和主题识别,经人工判读验证：基于引文内容分析的高被引论文识别的核心主题能够较好地揭示高被引论文的被引原因（引用动机）,而且与论文的研究内容相符合;与基于全文、基于标题和摘要的主题识别相比,在引文内容分析基础上识别的主题具有更好的主题代表性,能够有效揭示被引文献的研究内容,是对原文相关信息的重要补充。本文的实验表明基于引文内容分析的高被引论文主题识别是可行而且有效的。图4。表4。参考文献31。     

Perceptions of women managers and their communicator competencies 1

A stepfamily often faces unique challenges that can influence the strength of its bonds. One way in which stepfamily members adapt to these challenges is through communication. This study investigates adolescents’ and young adults’ use of topic avoidance in stepfamilies and its association with satisfaction and the parenting style of the stepparent. The results revealed that the more satisfied the participants were with their mother, father, stepmother, stepfather, and stepfamily overall, the less avoidance they were likely to employ. The findings also showed that more authoritarian stepparenting tended to increase the amount of avoidance, whereas more permissiveness and authoritativeness decreased it. Authoritativeness and authoritarianism were more strongly associated than permissiveness with participants’ avoidance. Perceptions of authoritarian stepparenting were also negatively associated with participants’ satisfaction with their stepparent. Finally, low or high degrees of persmissiveness were associated with low levels of satisfaction, but moderate degrees of permissive stepparenting were associated with moderate degrees of satisfaction with one's stepfamily.     

Cultural Variations in the Reasons People Provide Avoidance Support

This study explores variations in the reasons people engage in avoidance as a support strategy across the two cultures of the United States and China. An avoidance-support belief scale was developed. Exploratory factor analysis of responses from 231 Americans and 219 Chinese identified three dimensions of the scale: (1) protecting the support seeker from harm by avoiding, (2) maintaining a positive mood by avoiding, and (3) spiritual/philosophical reasons for avoiding. Follow-up analyses revealed that Chinese scored significantly and substantially higher than Americans for all three avoidance support dimensions. Implications and future research directions are discussed.     

专业搜索引擎的排序算法研究

探讨影响搜索引擎排序的一般性因素：词频和词位置信息、用户行为信息、网页之间的链接信息等，在此基础上针对专业搜索引擎的排序算法，提出主题相关度并结合基础教育搜索引擎进行实验。实验结果表明，专业搜索引擎中主题相关度的适当应用能明显改善排序结果。