Document summarization with neural query modeling

Document summarization is a natural language processing task that aims to produce a short summary that concisely delivers the most important information of a document or multiple documents. Over the last few decades, the task has drawn much attention from both academia and industry, as it provides effective tools to manage and access text information. For example, through a newswire summarization engine, users can quickly digest a cluster of news articles by reading a short summary of the topic. Such summaries can, meanwhile, be used by news recommendation and question answering engines. Depending on the users’ role in the summarization process, document summarization falls into two broad categories: generic summarization and query focused summarization (QFS). The former focuses on information intrinsically salient in the input text, while the latter also caters to requests explicitly specified by users. Despite the difference between generic summarization and QFS in their task formulations, we argue that all summaries address queries, even if they are not formulated explicitly. In this thesis, we introduce query modeling in the document summarization context as a critical objective for incorporating observed or latent user intent. We investigate different approaches that explore this theme with deep neural networks. We develop novel systems with neural query modeling for both extractive summarization, where summaries are composed of salient segments (e.g., sentences) from the original document(s), and abstractive summarization, where summaries are made up of words or phrases that do not exist in the input. The recent availability of large-scale datasets has driven the development of neural models that create generic summaries. However, training data in the form of queries, documents, and summaries for QFS is scarce. As most existing research in QFS has employed an extractive approach, we first consider better modeling query-cluster interactions for low-resource extractive QFS. In contrast to previous work with retrieval-style methods for assembling query-relevant summaries, we propose a framework that progressively estimates whether text segments should be included in the summary. Notably, modules of this framework can be independently developed and can leverage training data if available. We present an instantiation of this framework with distant supervision from question answering where various resources exist to identify segments which are likely to answer the query. Experiments on benchmark datasets show that our framework achieves competitive results and is robust across domains. Ideally, summaries should be abstracts, and the hidden costs incurred by annotating QA pairs should be avoided in query modeling. The second part of this thesis focuses on the low-resource challenge in abstractive QFS, and builds an abstractive QFS system which is trained query-free. Concretely, we propose to decompose the task into query modeling and conditional language modeling. For query modeling, we first introduce a unified representation for summaries and queries to exploit training resources in generic summarization, on top of which a weakly supervised model is optimized for evidence estimation. The proposed framework achieves state-of-the-art performance in generating query focused abstracts across existing benchmarks. Finally, the third part of this thesis moves beyond QFS. We provide a unified modeling framework for any kind of summarization, under the assumption that all summaries are a response to a query, which is observed in the case of QFS and latent in the case of generic summarization. We model queries as discrete latent variables over document tokens, and learn representations compatible with observed and unobserved query verbalizations. Requiring no further optimization on downstream summarization tasks, experiments show that our approach outperforms strong comparison systems across benchmarks, query types, document settings, and target domains

Muscle activation patterns in shoulder impingement patients

Introduction: Shoulder impingement is one of the most common presentations of shoulder joint problems 1. It appears to be caused by a reduction in the sub-acromial space as the humerus abducts between 60o -120o – the 'painful arc'. Structures between the humeral head and the acromion are thus pinched causing pain and further pathology 2. Shoulder muscle activity can influence this joint space but it is unclear whether this is a cause or effect in impingement patients. This study aimed to observe muscle activation patterns in normal and impingement shoulder patients and determine if there were any significant differences. Method: 19 adult subjects were asked to perform shoulder abduction in their symptomatic arm and non-symptomatic. 10 of these subjects (age 47.9 ± 11.2) were screened for shoulder impingement, and 9 subjects (age 38.9 ± 14.3) had no history of shoulder pathology. Surface EMG was used to collect data for 6 shoulder muscles (Upper, middle and lower trapezius, serratus anterior, infraspinatus, middle deltoids) which was then filtered and fully rectified. Subjects performed 3 smooth unilateral abduction movements at a cadence of 16 beats of a metronome set at 60bpm, and the mean of their results was recorded. T-tests were used to indicate any statistical significance in the data sets. Significance was set at P<0.05. Results: There was a significant difference in muscle activation with serratus anterior in particular showing a very low level of activation throughout the range when compared to normal shoulder activation patterns (<30%). Middle deltoid recruitment was significantly reduced between 60-90o in the impingement group (30:58%).Trends were noted in other muscles with upper trapezius and infraspinatus activating more rapidly and erratically (63:25%; 60:27% respectively), and lower trapezius with less recruitment (13:30%) in the patient group, although these did not quite reach significance. Conclusion: There appears to be some interesting alterations in muscle recruitment patterns in impingement shoulder patients when compared against their own unaffected shoulders and the control group. In particular changes in scapula control (serratus anterior and trapezius) and lateral rotation (infraspinatus), which have direct influence on the sub-acromial space, should be noted. It is still not clear whether these alterations are causative or reactionary, but this finding gives a clear indication to the importance of addressing muscle reeducation as part of a rehabilitation programme in shoulder impingement patients

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia