In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[EX Q]: A variety of real-world processes (over networks) produce sequences of data whose complex temporal dynamics need to be studied. More especially, the event timestamps can carry important information about the underlying network dynamics, which otherwise are not available from the time-series evenly sampled from continuous signals. Moreover, in most complex processes, event sequences and evenly-sampled times series data can interact with each other, which renders joint modeling of those two sources of data necessary. To tackle the above problems, in this paper, we utilize the rich framework of (temporal) point processes to model event data and timely update its intensity function by the synergic twin Recurrent Neural Networks (RNNs). In the proposed architecture, the intensity function is synergistically modulated by one RNN with asynchronous events as input and another RNN with time series as input. Furthermore, to enhance the interpretability of the model, the attention mechanism for the neural point process is introduced. The whole model with event type and timestamp prediction output layers can be trained end-to-end and allows a black-box treatment for modeling the intensity. We substantiate the superiority of our model in synthetic data and three real-world benchmark datasets.
[EX A]: Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks

[EX Q]: The utility of Twitter data as a medium to support populationlevel mental health monitoring is not well understood. In an effort to better understand the predictive power of supervised machine learning classifiers and the influence of feature sets for efficiently classifying depression-related tweets on a large-scale, we conducted two feature study experiments. In the first experiment, we assessed the contribution of feature groups such as lexical information (e.g., unigrams) and emotions (e.g., strongly negative) using a feature ablation study. In the second experiment, we determined the percentile of top ranked features that produced the optimal classification performance by applying a three-step feature elimination approach. In the first experiment, we observed that lexical features are critical for identifying depressive symptoms, specifically for depressed mood (-35 points) and for disturbed sleep (-43 points). In the second experiment, we observed that the optimal F1-score performance of top ranked features in percentiles variably ranged across classes e.g., fatigue or loss of energy (5th percentile, 288 features) to depressed mood (55th percentile, 3,168 features) suggesting there is no consistent count of features for predicting depressive-related tweets. We conclude that simple lexical features and reduced feature sets can produce comparable results to larger feature sets.
[EX A]: Feature Studies to Inform the Classification of Depressive Symptoms from Twitter Data for Population Health

[EX Q]: Summarization of large texts is still an open problem in language processing. In this work we develop a full fledged pipeline to generate summaries of news articles using the Abstract Meaning Representation(AMR). We first generate the AMR graphs of stories then extract summary graphs from the story graphs and finally generate sentences from the summary graph. For extracting summary AMRs from the story AMRs we use a two step process. First, we find important sentences from the text and then extract the summary AMRs from those selected sentences. We outperform the previous methods using AMR for summarization by more that 3 ROGUE-1 points. On the CNN-Dailymail corpus we achieve results competitive with the strong lead-3 baseline till summary graph extraction step.
[EX A]:
Text Summarization using Abstract Meaning Representation