HIMEG: HIERARCHICAL MEETING NOTE GENERATION USING TEXT SEGMENTATION AND ATTENTION CORRELATION

Techniques are presented herein that support a HiMEG system, a framework that helps create meeting notes of multiple granularities for meeting invitees so that they can refresh their memory or catch up on any meeting. Such a system comprises a Segmentation Engine that may divide a meeting transcript into separate sections representing the different topics that were covered during a meeting. Such a system also comprises an Attention Correlation Analyzing Model that may be used to capture the attention correlation between different meeting notes that were generated from the discovered topics, which is useful in a Meeting Note Summarization Model that may assess which meeting notes are most similar. Under such a system, one effective summary may be formed based on the most similar meeting notes and the process may be repeated until there is one overall summary of a meeting. In the end, a user may read the high-level summary of a meeting and then dive further into the specific contents of the general meeting note based on their interests and needs. While the above-described framework was originally developed for generating meeting notes, it may also be applied to any text input such as speeches, action scripts, and training scripts

STRATIFIED INVESTIGATION OF LOW-PERFORMING NETWORK ARCHITECTURE (SINA) USING GRAPH NEURAL NETWORKS AND PEER BENCHMARKING

Monitoring and troubleshooting networks to improve their performance and reliability is a complicated task, not only because it requires the checking of every single network device but also because it involves understanding the connections between those devices. To address that complexity, techniques are presented herein that support a Stratified Investigation of Low-Performing Network Architecture (SINA) system. Such a system is a framework that identifies any low-performing network architecture areas and makes improvements on a subnetwork level. Such a system may automatically identify low-performing areas of a customer’s network based on information about similar networks and expert knowledge with solutions. Such a system may employ a graph neural network (GNN) to identify areas of a customer’s network that need improvement based on a calculated performance score while considering the interaction between devices and the topology of networks. Further, such a system may leverage network performance metrics from many customers to create a performance benchmark and then evaluate where a customer’s network’s performance lies within that benchmark. Still further, such a system may employ a transformer-based natural language processing (NLP) model (that understands key semantic knowledge from documents, device configurations, and logs) to help generate solutions to network issues. Finally, based on high-performing customers relative to the benchmark and documents with best practices for configuring networks, a SINA system may provide solutions to a customer’s network that will help optimize network performance

Looking for Gold in the Sands: Stock Prediction Using Financial News and Social Media

Both traditional finance and behavioral finance theory have reached a consensus that the news media de facto influence stock prices to some extent. There is also evidence that investors are not only subject to the sentiment of related news articles but also the public opinions. The challenge lies on how to quantify such sentimental information to predict the movement of stock market. To measure the sentiments of articles and capture the public mood from postings, we construct and maintain a sentiment dictionary. We utilize both the official information from news articles and user postings in discussion boards to predict firm-specific stock price, and differentiate various types of news articles in the predictive model. Our experiments on CSI 100 stocks during a six week period show a predictive performance in closeness to the actual future stock price is 0.03503 in terms of mean squared error, the same direction of price movement as the future price is 67.6%. Among all seven news topic categories, restructuring news of enterprises has the best predicting performance with direction accuracy of 68.18%

A Sentiment-based Hybrid Model for Stock Return Forecasting

Real-world financial time series often contain both linear and nonlinear patterns. However, traditional time series analysis models, such as ARIMA, hold the assumption that a linear correlation exists among time series values while leaving nonlinear relation into error terms. Based on financial theories, we argue that investor sentiment is the main contributor to nonlinear pattern of stock time series. Furthermore, we propose a sentiment-based hybrid model (SLNM) to better capture nonlinear information in stock time series. According to the forecasting experimental results, SLNM exhibits the sensitivity to sentiment environments, which in turn supports the argument that investor sentiment is the main source of nonlinear pattern in stock time series. For those stocks that are in top 10 of CAR Ranking List ─ these stocks are more likely pursed by emotional investors and thus in optimistic sentiment environment, SLNM improves forecasting performance dramatically: Increase Direction Accuracy by 40% and reduce RMSE by 19.3%. While, for those that are in bottom 10 of CAR Ranking List─ these stocks defer more emotional investors from further participating in stock trading and thus in pessimistic sentiment environment, SLNM has a fair improvement on performance: Hold the similar Direction Accuracy and reduce RMSE only by 2.5%. All these indicate that investor sentiment play a key role in stock return forecasting. Our work sheds light on the research of sentiment-based prediction models