Controllable Topic-Focused Abstractive Summarization

Controlled abstractive summarization focuses on producing condensed versions of a source article to cover specific aspects by shifting the distribution of generated text towards a desired style, e.g., a set of topics. Subsequently, the resulting summaries may be tailored to user-defined requirements. This paper presents a new Transformer-based architecture capable of producing topic-focused summaries. The architecture modifies the cross-attention mechanism of the Transformer to bring topic-focus control to the generation process while not adding any further parameters to the model. We show that our model sets a new state of the art on the NEWTS dataset in terms of topic-focused abstractive summarization as well as a topic-prevalence score. Moreover, we show via extensive experiments that our proposed topical cross-attention mechanism can be plugged into various Transformer models, such as BART and T5, improving their performance on the CNN/Dailymail and XSum benchmark datasets for abstractive summarization. This is achieved via fine-tuning, without requiring training from scratch. Finally, we show through human evaluation that our model generates more faithful summaries outperforming the state-of-the-art Frost model

Neural Summarization of Electronic Health Records

Hospital discharge documentation is among the most essential, yet time-consuming documents written by medical practitioners. The objective of this study was to automatically generate hospital discharge summaries using neural network summarization models. We studied various data preparation and neural network training techniques that generate discharge summaries. Using nursing notes and discharge summaries from the MIMIC-III dataset, we studied the viability of the automatic generation of various sections of a discharge summary using four state-of-the-art neural network summarization models (BART, T5, Longformer and FLAN-T5). Our experiments indicated that training environments including nursing notes as the source, and discrete sections of the discharge summary as the target output (e.g. "History of Present Illness") improve language model efficiency and text quality. According to our findings, the fine-tuned BART model improved its ROUGE F1 score by 43.6% against its standard off-the-shelf version. We also found that fine-tuning the baseline BART model with other setups caused different degrees of improvement (up to 80% relative improvement). We also observed that a fine-tuned T5 generally achieves higher ROUGE F1 scores than other fine-tuned models and a fine-tuned FLAN-T5 achieves the highest ROUGE score overall, i.e., 45.6. For majority of the fine-tuned language models, summarizing discharge summary report sections separately outperformed the summarization the entire report quantitatively. On the other hand, fine-tuning language models that were previously instruction fine-tuned showed better performance in summarizing entire reports. This study concludes that a focused dataset designed for the automatic generation of discharge summaries by a language model can produce coherent Discharge Summary sections

Just-in-time information retrieval and summarization for personal assistance

With the rapid development of means for producing user-generated data opportunities for collecting such data over a time-line and utilizing it for various human-aid applications are more than ever. Wearable and mobile data capture devices as well as many online data channels such as search engines are all examples of means of user data collection. Such user data could be utilized to model user behavior, identify relevant information to a user and retrieve it in a timely fashion for personal assistance. User data can include recordings of one's conversations, images, biophysical data, health-related data captured by wearable devices, interactions with smartphones and computers, and more. In order to utilize such data for personal assistance, summaries of previously recorded events can be presented to a user in order to augment the user's memory, send notifications about important events to the user, predict the user's near-future information needs and retrieve relevant content even before the user asks. In this PhD dissertation, we design a personal assistant with a focus on two main aspects: The first aspect is that a personal assistant should be able to summarize user data and present it to a user. To achieve this goal, we build a Social Interactions Log Analysis System (SILAS) that summarizes a person's conversations into event snippets consisting of spoken topics paired with images and other modalities of data captured by the person's wearable devices. Furthermore, we design a novel discrete Dynamic Topic Model (dDTM) capable of tracking the evolution of the intermittent spoken topics over time. Additionally, we present the first neural Customizable Abstractive Topic-based Summarization (CATS) model that produces summaries of textual documents including meeting transcripts in the form of natural language. The second aspect that a personal assistant should be capable of, is proactively addressing the user's information needs. For this purpose, we propose a family of just-in-time information retrieval models such as an evolutionary model named Kalman combination of Recency and Establishment (K2RE) that can anticipate a user's near-future information needs. Such information needs can include information for preparing a future meeting or near-future search queries of a user

Investigating the Effectiveness of Positivism Group Psychotherapy Based on Frisch's Theory in Quality of Life of Breast Cancer Patients

The aim of conducting this research was to investigate the effectiveness of positivism group psychotherapy based on Frisch's theory in quality of life of breast cancer patients referring to the counseling centers in Mashhad, Iran. This is a quasi-experimental study with pre- and post-tests in which experimental and control groups were utilized. The statistical populations were breast cancer patients in Iran in 2016. The research sample included 30 breast cancer patients who were randomly selected and divided into experimental and control groups. The experimental group received the psychotherapy approach based on Frisch’s theory in eight weekly sessions. The quality of life questionnaire in patients with breast cancerOL-BC was used as the measurement tool.  The data which were analyzed using analysis of covariance (ANCOVA) procedure, considered statistically significant (P>0.5). Results showed that there is a significant difference between the mean scores of experimental and control groups in the post-test. This means that the positivism group psychotherapy based on Frisch's theory was effective in promoting the quality of life of breast cancer patients (P> 0.01). Furthermore, the results showed that positivism group psychotherapy based on Frisch's theory increased post-test scores of experimental group in aspects of  physical, mental, social and environmental health (P <0.05). Results confirmed the positive effect of positivism group psychotherapy based on Frisch's theory on breast cancer patients’ quality of life

The relationship between trusting God and mental health in medical students of Shahid Beheshti University of Tehran

 For downloading the full-text of this article please click here.Background and Objectives: According to religious instructions, trusting God is the best reason to provide mental health, and research in this area confirms this reality. Students as the most important segments of society are now at risk of mental health problems. The purpose of this study was to evaluate the relationship between trusting God and mental health on medical students at Shahid Beheshti University of Tehran.Materials and Methods: The type of this study is descriptively (field study) and sampling method is available sampling. In this study, 400 students of Shahid Beheshti University of Medical Sciences in Tehran were randomly selected. Data collection techniques are trusting God Questionnaire (seeking disasters and live events) & SCL- 90 (Symptom Checklist 90). Data were analyzed using SPSS software and descriptive statistics (mean and standard deviation) and inferential statistics (Pearson correlation).Results: The findings show that a significant correlation between trusting God and mental health (P< 0.05). It was observed statistically significant correlation between trusting God and mental health in variables of depression, anxiety, somatization, interpersonal sensitivity and aggressiveness (-0.21, -0.22, - 0.31,- 0.26, and -0.16) and need to be clarified. Also there was a significant correlation between sub - scale trusting God Inventory (Assignment, Others, Self) and mental health Inventory (-0.28, -0.27, -0.30).Conclusion: The results of this study showed that, there is a strong correlation between trusting God & mental health, and trusting God as a psychological reinforcement, is one of the most effective Coping mechanisms spiritual – religious that can be used to provide mental health promotion and psychological well-being of community.Keywords:  Trust in God, mental health, studentsFor downloading the full-text of this article please click here.

Improved Rheological Model of Oil-Based Drilling Fluid for South-western Iranian Oilfields

In this study, predictive capabilities of apparent viscosity of oil-based drilling fluids which is used in National Iranian South Oilfields Company (NISOC) were evaluated using Newtonian and non-Newtonian models to drive a new suitable equation. The non-Newtonian models include Bingham plastic, Power law, Herschel-Bulkley, Casson, and Robertson-Stiff. To validate the results, the calculated viscosity from rheology models was compared to the fann 35 data of viscometer. The results showed that Robertson-Stiff model has the best prediction of shear stress and viscosity with an absolute average percent error of 3.58. This was followed by Herschel-Bulkley, Casson, Power law, Bingham plastic, and Newtonian with the absolute average percent error of 3.68, 3.77, 9.04, 20.09, and 44.02 respectively. Therefore, the new equation was proposed to predict the shear stress for oil-based drilling fluids which is used in Southwestern Iranian Oilfields. In comparison to the results of the experimental data of this study, it was revealed that the proposed equation has a good agreement with the real shear stress and apparent viscosities

Validation of a short form Wisconsin Upper Respiratory Symptom Survey (WURSS-21)

<p>Abstract</p> <p>Background</p> <p>The Wisconsin Upper Respiratory Symptom Survey (WURSS) is an illness-specific health-related quality-of-life questionnaire outcomes instrument.</p> <p>Objectives</p> <p>Research questions were: 1) How well does the WURSS-21 assess the symptoms and functional impairments associated with common cold? 2) How well can this instrument measure change over time (responsiveness)? 3) What is the minimal important difference (MID) that can be detected by the WURSS-21? 4) What are the descriptive statistics for area under the time severity curve (AUC)? 5) What sample sizes would trials require to detect MID or AUC criteria? 6) What does factor analysis tell us about the underlying dimensional structure of the common cold? 7) How reliable are items, domains, and summary scores represented in WURSS? 8) For each of these considerations, how well does the WURSS-21 compare to the WURSS-44, Jackson, and SF-8?</p> <p>Study Design and Setting</p> <p>People with Jackson-defined colds were recruited from the community in and around Madison, Wisconsin. Participants were enrolled within 48 hours of first cold symptom and monitored for up to 14 days of illness. Half the sample filled out the WURSS-21 in the morning and the WURSS-44 in the evening, with the other half reversing the daily order. External comparators were the SF-8, a 24-hour recall general health measure yielding separate physical and mental health scores, and the eight-item Jackson cold index, which assesses symptoms, but not functional impairment or quality of life.</p> <p>Results</p> <p>In all, 230 participants were monitored for 2,457 person-days. Participants were aged 14 to 83 years (mean 34.1, SD 13.6), majority female (66.5%), mostly white (86.0%), and represented substantive education and income diversity. WURSS-21 items demonstrated similar performance when embedded within the WURSS-44 or in the stand-alone WURSS-21. Minimal important difference (MID) and Guyatt's responsiveness index were 10.3, 0.71 for the WURSS-21 and 18.5, 0.75 for the WURSS-44. Factorial analysis suggested an eight dimension structure for the WURSS-44 and a three dimension structure for the WURSS-21, with composite reliability coefficients ranging from 0.87 to 0.97, and Cronbach's alpha ranging from 0.76 to 0.96. Both WURSS versions correlated significantly with the Jackson scale (W-21 R = 0.85; W-44 R = 0.88), with the SF-8 physical health (W-21 R = -0.79; W-44 R = -0.80) and SF-8 mental health (W-21 R = -0.55; W-44 R = -0.60).</p> <p>Conclusion</p> <p>The WURSS-44 and WURSS-21 perform well as illness-specific quality-of-life evaluative outcome instruments. Construct validity is supported by the data presented here. While the WURSS-44 covers more symptoms, the WURSS-21 exhibits similar performance in terms of reliability, responsiveness, importance-to-patients, and convergence with other measures.</p

Relationship of Internet addiction with self-esteem and depression in university students

Background. The aim of the study was to investigate the rela- tionship of self-esteem and depression with Internet addiction in university students. Methods. The present descriptive-analytic correlation study involved 408 students (150 female and 258 male) who had been selected by means of a cluster sampling method from among all the students studying in Birjand Islamic Azad University. Students were evalu- ated through the Beck Depression Inventory (BDI), Cooper Smith Self-Esteem Inventory (CSEI) and Internet Addiction Test (IAT). Results. The results indicated that 40.7% of the students had Internet addiction. A significant correlation emerged between depression, self-esteem and internet addiction. Regression analy- sis indicated that depression and self-esteem were able to predict the variance of Internet addiction to some extent. Conclusions. It may be important to evaluate self-esteem and depression in people with Internet addiction. These variables should be targeted for effective cognitive behavioral therapy in people with Internet addiction