Helpfulness Guided Review Summarization

User-generated online reviews are an important information resource in people's everyday life. As the review volume grows explosively, the ability to automatically identify and summarize useful information from reviews becomes essential in providing analytic services in many review-based applications. While prior work on review summarization focused on different review perspectives (e.g. topics, opinions, sentiment, etc.), the helpfulness of reviews is an important informativeness indicator that has been less frequently explored. In this thesis, we investigate automatic review helpfulness prediction and exploit review helpfulness for review summarization in distinct review domains. We explore two paths for predicting review helpfulness in a general setting: one is by tailoring existing helpfulness prediction techniques to a new review domain; the other is by using a general representation of review content that reflects review helpfulness across domains. For the first one, we explore educational peer reviews and show how peer-review domain knowledge can be introduced to a helpfulness model developed for product reviews to improve prediction performance. For the second one, we characterize review language usage, content diversity and helpfulness-related topics with respect to different content sources using computational linguistic features. For review summarization, we propose to leverage user-provided helpfulness assessment during content selection in two ways: 1) using the review-level helpfulness ratings directly to filter out unhelpful reviews, 2) developing sentence-level helpfulness features via supervised topic modeling for sentence selection. As a demonstration, we implement our methods based on an extractive multi-document summarization framework and evaluate them in three user studies. Results show that our helpfulness-guided summarizers outperform the baseline in both human and automated evaluation for camera reviews and movie reviews. While for educational peer reviews, the preference for helpfulness depends on student writing performance and prior teaching experience

The Service Quality Evaluation of Mobile Communication from Quality Improvement Perspective ----a case study on China telecom in Wuchang District Wuhan City

Based on SERVAUAL model, this paper brings in the entropy method to rank quality improvement (QI) priority for service attributes, and a service quality evaluation(SQE) model integrating competitive analyses has been structured to evaluate the mobile communication service quality (SQ) for Wuhan Branch of China Telecom(WBCT). The research shows that the QI priority of 22 service attributes has changed as adopts entropy method comparing with gap-based SERVQUAL. The service attributes that finally should be improved have changed from Q20(Various business charges reasonable) and Q22(Record customer complaints and improve) to Q21(provide customers all kinds of value-added services) and Q11(Staff serves with high efficiency)

Impact of Annotation Difficulty on Automatically Detecting Problem Localization of Peer-Review Feedback

We believe that providing assessment on students ’ reviewing performance will enable students to improve the quality of their peer reviews. We focus on assessing one particular aspect of the textual feedback contained in a peer review – the presence or absence of problem localization; feedback containing problem localization has been shown to be associated with increased understanding and implementation of the feedback. While in prior work we demonstrated the feasibility of learning to predict problem localization using linguistic features automatically extracted from textual feedback, we hypothesize that inter-annotator disagreement on labeling problem localization might impact both the accuracy and the content of the predictive models. To test this hypothesis, we compare the use of feedback examples where problem localization is labeled with differing levels of annotator agreement, for both training and testing our models. Our results show that when models are trained and tested using only feedback where annotators agree on problem localization, the models both perform with high accuracy, and contain rules involving just two simple linguistic features. In contrast, when training and testing using feedback examples where annotators both agree and disagree, the model performance slightly drops, but the learned rules capture more subtle patterns of problem localization. Keywords problem localization in text comments, data mining of peer reviews, inter-annotator agreement, natural langua

Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN

Layer-wise Relevance Propagation (LRP) and saliency maps have been recently used to explain the predictions of Deep Learning models, specifically in the domain of text classification. Given different attribution-based explanations to highlight relevant words for a predicted class label, experiments based on word deleting perturbation is a common evaluation method. This word removal approach, however, disregards any linguistic dependencies that may exist between words or phrases in a sentence, which could semantically guide a classifier to a particular prediction. In this paper, we present a feature-based evaluation framework for comparing the two attribution methods on customer reviews (public data sets) and Customer Due Diligence (CDD) extracted reports (corporate data set). Instead of removing words based on the relevance score, we investigate perturbations based on embedded features removal from intermediate layers of Convolutional Neural Networks. Our experimental study is carried out on embedded-word, embedded-document, and embedded-ngrams explanations. Using the proposed framework, we provide a visualization tool to assist analysts in reasoning toward the model's final prediction.Comment: NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, Montr\'eal, Canad

The botanical origin and antioxidant, anti-BACE1 and antiproliferative properties of bee pollen from different regions of South Korea

Abstract Background Bee pollen (BP) has been used as a traditional medicine and food diet additive due to its nutritional and biological properties. The potential biological properties of bee pollen vary greatly with the botanical and geographical origin of the pollen grains. This study was conducted to characterize the botanical origin and assess the antioxidant effects of ethanol extracts of 18 different bee pollen (EBP) samples from 16 locations in South Korea and their inhibitory activities on human β-amyloid precursor cleavage enzyme (BACE1), acetylcholinesterase (AChE), human intestinal bacteria, and 5 cancer cell lines. Methods The botanical origin and classification of each BP sample was evaluated using palynological analysis by observing microscope slides. We measured the biological properties, including antioxidant capacity, inhibitory activities against human BACE1, and AChE, and antiproliferative activities toward five cancer cell lines, of the 18 EBPs. In addition, the growth inhibitory activities on four harmful intestinal bacteria, six lactic acid-producing bacteria, two nonpathogenic bacteria, and an acidulating bacterium were also assessed. Results Four samples (BP3, BP4, BP13 and BP15) were found to be monofloral and presented four dominant pollen types: Quercus palustris, Actinidia arguta, Robinia pseudoacacia, and Amygdalus persica. One sample (BP12) was found to be bifloral, and the remaining samples were considered to be heterofloral. Sixteen samples showed potent antioxidant activities with EC50 from 292.0 to 673.9 μg mL− 1. Fourteen samples presented potent inhibitory activity against human BACE1 with EC50 from 236.0 to 881.1 μg mL− 1. All samples showed antiproliferative activity toward the cancer cell lines PC-3, MCF-7, A549, NCI-H727 and AGS with IC50 from 2.7 to 14.4 mg mL− 1, 0.9 to 12.7 mg mL− 1, 5.0 to > 25 mg mL− 1, 2.7 to 17.7 mg mL− 1, and 2.4 to 8.7 mg mL− 1, respectively. In addition, total phenol and flavonoid contents had no direct correlation with antioxidant, anti-human BACE1, or antiproliferative activities. Conclusion Fundamentally, Korean bee pollen-derived preparations could be considered a nutritional addition to food to prevent various diseases related to free radicals, neurodegenerative problems, and cancers. The botanical and geographical origins of pollen grains could help to establish quality control standards for bee pollen consumption and industrial production

A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss

Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms. Review summarization aims at generating a concise summary that describes the key opinions and sentiment of a review, while sentiment classification aims to predict a sentiment label indicating the sentiment attitude of a review. To effectively leverage the shared sentiment information in both review summarization and sentiment classification tasks, we propose a novel dual-view model that jointly improves the performance of these two tasks. In our model, an encoder first learns a context representation for the review, then a summary decoder generates a review summary word by word. After that, a source-view sentiment classifier uses the encoded context representation to predict a sentiment label for the review, while a summary-view sentiment classifier uses the decoder hidden states to predict a sentiment label for the generated summary. During training, we introduce an inconsistency loss to penalize the disagreement between these two classifiers. It helps the decoder to generate a summary to have a consistent sentiment tendency with the review and also helps the two sentiment classifiers learn from each other. Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.Comment: Accepted by SIGIR 2020. Updated the results of balanced accuracy scores in Table 3 since we found a bug in our source code. Nevertheless, our model still achieves higher balanced accuracy scores than the baselines after we fixed this bu

Ultra-low-dose spectral-detector computed tomography for the accurate quantification of pulmonary nodules: an anthropomorphic chest phantom study

PURPOSETo assess the quantification accuracy of pulmonary nodules using virtual monoenergetic images (VMIs) derived from spectral-detector computed tomography (CT) under an ultra-low-dose scan protocol.METHODSA chest phantom consisting of 12 pulmonary nodules was scanned using spectral-detector CT at 100 kVp/10 mAs, 100 kVp/20 mAs, 120 kVp/10 mAs, and 120 kVp/30 mAs. Each scanning protocol was repeated three times. Each CT scan was reconstructed utilizing filtered back projection, hybrid iterative reconstruction, iterative model reconstruction (IMR), and VMIs of 40–100 keV. The signal-to-noise ratio and air noise of images, absolute differences, and absolute percentage measurement errors (APEs) of the diameter, density, and volume of the four scan protocols and ten reconstruction images were compared.RESULTSWith each fixed reconstruction image, the four scanning protocols exhibited no significant differences in APEs for diameter and density (all P > 0.05). Of the four scan protocols and ten reconstruction images, APEs for nodule volume had no significant differences (all P > 0.05). At 100 kVp/10 mAs, APEs for density using IMR were the lowest (APE-mean: 6.69), but no significant difference was detected between VMIs at 50 keV (APE-mean: 11.69) and IMR (P = 0.666). In the subgroup analysis, at 100 kVp/10 mAs, there were no significant differences between VMIs at 50 keV and IMR in diameter and density (all P > 0.05). The radiation dose at 100 kVp/10 mAs was reduced by 77.8% compared with that at 120 kVp/30 mAs.CONCLUSIONCompared with IMR, reconstruction at 100 kVp/10 mAs and 50 keV provides a more accurate quantification of pulmonary nodules, and the radiation dose is reduced by 77.8% compared with that at 120 kVp/30 mAs, demonstrating great potential for ultra-low-dose spectral-detector CT

Antenatal depression is associated with perceived stress, family relations, educational and professional status among women in South of China: a multicenter cross-sectional survey

BackgroundAntenatal depression is a commonly seen mental health concern for women. This study introduced a multicenter cross-sectional survey with a large sample to provide new insights into pregnant women’s depression, its socio-demographic and obstetric characteristics correlates, and its perceived stress among Chinese pregnant women.MethodsThis study conducted an observational survey according to the STROBE checklist. The multicenter cross-sectional survey was performed from August 2020 to January 2021 by distributing paper questionnaires among pregnant women from five tertiary hospitals in South China. The questionnaire included socio-demographic and obstetrics information, the Edinburgh Postnatal Depression Scale, and the 10-item Perceived Stress Scale. For the analyses, the Chi-square test and Multivariate logistic regression were utilized.ResultsAmong 2014 pregnant women in their second/third trimester, the prevalence of antenatal depression was 36.3%. 34.4% of pregnant women reported AD in their second trimester of pregnancy, and 36.9% suffered from AD in third trimester of pregnancy. A multivariate logistic regression model indicated that unemployed women, lower levels of education, poor marital relationships, poor parents-in-law relationships, concerns about contracting COVID-19, and higher perceived stress could aggravate antenatal depression among participants (p<0.05).ConclusionThere is a high proportion of antenatal depression among pregnant women in South China, so integrating depression screening into antenatal care services is worthwhile. Maternal and child health care providers need to evaluate pregnancy-related risk factors (perceived stress), socio-demographic factors (educational and professional status), and interpersonal risk factors (marital relations and relationship with Parents-in-law). In future research, the study also emphasized the importance of providing action and practical support to reduce the experience of antenatal depression among disadvantaged sub-groups of pregnant women

Chk1 Inhibition Ameliorates Alzheimer's Disease Pathogenesis and Cognitive Dysfunction Through CIP2A/PP2A Signaling

Alzheimer's disease (AD) is the most common neurodegenerative disease with limited therapeutic strategies. Cell cycle checkpoint protein kinase 1 (Chk1) is a Ser/Thr protein kinase which is activated in response to DNA damage, the latter which is an early event in AD. However, whether DNA damage-induced Chk1 activation participates in the development of AD and Chk1 inhibition ameliorates AD-like pathogenesis remain unclarified. Here, we demonstrate that Chk1 activity and the levels of protein phosphatase 2A (PP2A) inhibitory protein CIP2A are elevated in AD human brains, APP/PS1 transgenic mice, and primary neurons with A beta treatment. Chk1 overexpression induces CIP2A upregulation, PP2A inhibition, tau and APP hyperphosphorylation, synaptic impairments, and cognitive memory deficit in mice. Moreover, Chk1 inhibitor (GDC0575) effectively increases PP2A activity, decreases tau phosphorylation, and inhibits A beta overproduction in AD cell models. GDC0575 also reverses AD-like cognitive deficits and prevents neuron loss and synaptic impairments in APP/PS1 mice. In conclusion, our study uncovers a mechanism by which DNA damage-induced Chk1 activation promotes CIP2A-mediated tau and APP hyperphosphorylation and cognitive dysfunction in Alzheimer's disease and highlights the therapeutic potential of Chk1 inhibitors in AD